فصلنامه پژوهش های ژئومورفولوژی کمی
سال نهم شماره 4 (پیاپی 36، بهار 1400)

There are many different types of the terrain visualization techniques, which are the result of the evolution over time and the development of technological innovations. Earth’s surface roughness plays a key role in controlling surface and the Earth's atmosphere processes. This relationship is so strong that understanding the nature of the terrain topography can directly lead to clarity in understanding of these processes, both analytically and computationally. Therefore, the analysis and visualization of the terrain topography has provided significant applications in many of the activities of the Geographic Information System and environmental Modeling. Geomorphologists and cartographers both use information derived from digital elevation models to quantify the shape and structure of the Earth's topographic surfaces. Cartographic purpose of terrain relief visualization is to display landforms and features of the earth’s surface which is done through drawing hachures and other methods that lead to more realistic terrain surface visualization. Cartographers have used a variety of mechanisms, including colors and shades to create a 3-dimensional appearance of topographical surface in 2-dimensional surface of the maps. The recent study tries to present different ideas and models in the section of terrain topography visualization. For this purpose, vector (point and line) and raster data structure, Digital elevation model with respecting to the concepts of geomorphometry and digital terrain modeling had been used. And python programming has been widely used to design and automating the algorithms of the models.

In the first part, the raster database was prepared and arranged. The raster database contains elevation data that includes digital surface model. Which is currently the most accurate elevation data on a free, global scale. digital Surface model released by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 23 meters were used to model the visualization of terrain topography surfaces. This data is obtained from the ALOS satellite images which is extracted from a five-meter grid data with global coverage which is now the most accurate elevation data in the global scale. In the next step, slope calculated using four slope algorithms on a digital surface model grid structure. The aspect was calculated in the same way. The aspect layer with the results of these four slope algorithms, averaged and standard hill shade calculated using common formula in the geographic information system. Then, the visualization modeling of the terrain topographic surfaces followed in two parts. In the vector modeling section, the hachure modeling of the terrain surface carried out which the results in the field of linear hachure drawing includes: Max-Center-Min, Max-Min, Random Point – Min and multi-sector aspect hachuring models which coded in the python programming environment. In the Point hachure section, two random point models with slope weight and random point model with curvature as a weight were modeled, coded and executed. In the raster modeling section, three model approaches were considered. In the first approach, blended shading was followed, which included combining standard hill shading model with a variety of terrain curvature, radiation models (Morphoradiation), and ridgeline effect models.14 types of terrain curvature were used and combined with standard hill shading to create new models for visualization of terrain surface topography. In the second part, radiation models including direct radiation, direct radiation duration, diffuse radiation and global radiation were combined with standard hill shading to create new models. To add the effect of the ridgelines in the display of the terrain surface topography, the method provided by Solhi and Seif (1399) was used to identify the ridges of the terrain topography.

Various models were prepared and presented in the terrain surface topography visualization. Part of the modeling is related to the vector data structures and the other part is related to the raster data structures. In the vector modeling section, four algorithms including: Max-Center-Min, Max-Min, Random point-Min and Multi-Sector Aspect hachuring were configured and presented. These four algorithms have the ability to create linear hachures on the terrain topography surfaces in order to create a spatial dimension of the digital elevation model. All four algorithms are performed using the moving window technique and automatically apply linear hachuring with using digital elevation model. The second part is related to raster data structures, which includes hybrid models (combining standard hill shade with different terrain curvatures types, radiation models and ridgeline effect), hypsometric tinting, contour maquette, and colored contour maquette.

Terrain surface topography visualization has wide applications and plays important role in the cartography and preparation of base-maps, geological maps and topographic maps as well as thematic maps such as geomorphological maps and many other maps. Modeling terrain surface topography, using different ideas, methods, and techniques, can be effective in improving and the visualization of terrain surfaces in different applications. In this research, with emphasis on the subject of terrain visualization modeling, different methods and models were presented both in the section of vector data structures (linear and dotted) and raster data structures. Various methods and models have been proposed that can be used in a wide range of environmental studies as well as cartographic methods and techniques. The presented models are practiced only from the digital elevation model and do not require field and special data, which are considered as features and strengths of these models. Future researchers are advised to develop and evolve the models, methods and techniques of this field of study and try to create practical and creative models. The field of digital terrain modeling, in the analytical and demonstration sections, creates a suitable platform for environmental science studies and in the form of basic and fundamental research can lead to scientific creation and dynamism in this field.

The sediments of the Playa lake are sensitive indicators of local climates that any change in climate, hydrology, and sedimentary environment will cause changes in the physical and chemical properties of the sediments. These sediments create a valuable and important archive with high resolution to examine these changes in the past. One method of studying climate change during quaternary and often Holocene is the study of clay minerals in sedimentary cores harvested from wetlands and lakes. The study of clay minerals in these sediments can help to more accurately identify and re-read their past history and be used to determine the severity of weathering processes and also to investigate possible climate change. Clay minerals are highly efficient in hot and humid environments with high chemical decomposition, for detecting climate change, and in cold environments as a factor in identifying the source of sediments.

To perform this study, 16 sedimentary cores were harvested using a manual core drilling equipment with an average depth of 7 m and a maximum depth of 11.5 m from the sub sediments of the Gavkhoni playa and surrounding areas. The core was described based on texture, sedimentary structure, and layering characteristics, color, plant and shell remains, the type of evaporative crystals, and the relative degree of hardening of the sediments, and their chronological column was plotted. 90 samples from 9 sedimentary cores were prepared for X-ray diffraction analysis (XRD) and sent to the laboratory. Also, to extract the age of sedimentary sequences, three samples of C14-AMS bulk sedimentary materials were analyzed and calibrated with OxCal software (Bronk Ramesy, 2013) with an error range of 2 Sigma and confidence level of 95%.

The results of X-ray diffraction show that the sediments in the Gavkhoni playa in order of abundance contain clay ellite, chlorite, montmorillonite, and kaolinite deposits, respectively. They are also seen as the main minerals of quartz, calcite, feldspar, evaporative and dolomite minerals in graph peaks. According to the results of the metering analysis, the sedimentation rate in the western parts is about 0.4 mm per year and in the eastern parts is 0.25 mm per year. The clay minerals of Sepiolite, Polygorskite, and Kunzite have not been observed in sedimentary cores. These minerals are specific to the diagenetic environments, indicating no effect of very low effect of the conversion type of diagenesis in Gavkhoni playa deposits. Therefore, considering the assurance of the ineffectiveness of conversion diagenesis, it is possible to achieve long-term climate at different times. Ellite and chlorite minerals in the cores of the northern part of Playa are mainly due to the weathering of the basic masses of north Playa (koh siah) and the weathering of volcanic rocks and low-grade metamorphic rocks in the Urmia-Dokhtar zone. The presence of kaolinite and the increase of phonetic minerals, especially quartz, in the two cores of G-13 and G-11 at depths of more than 3 meters indicate the presence of high-volume river flows in the region, which indicates humid and warm climatic periods. The presence of montmorillonite and ilite in the central cores of Playa with the age of more than 25000, evokes cold and temperate conditions in the late Pleistocene era in the region. The high water period in the late Pleistocene (about 18000 years ago) is marked by the precession od shore lines to land in the G-11 core with the presence of kaolinite at depths of more than 4 m. The wet conditions have gradually decreased from about 18000 to 12000 years ago. During this period, kaolinite is replaced by montmorillonite, which indicates a decrease in rainfall compared to the previous period. The predominant minerals of the early Holocene in the Gavkhoni playa are the illite and chlorite, indicating semi-dry conditions. During this time, the Montmorillonite mineral can be observed in the G-2, G-4, G-11 and G-13 cores, which is in good agreement with the delta facies at a depth of 3 to 4 meters in the G-2 and G-4 cores. The existence of a dry period about 8000 years ago is evident by the increase in evaporative minerals, the absence of kaolinite and montmorillonite, and the spread of dune sands at a depth of about 2 to 3 meters in the cores of the western and central parts is evident. 4000 years ago, the northern cores (Zayandeh Rud estuary) showed wet conditions with the presence of montmorillonite, illite and chlorite. This situation has continued for about a thousand years, after which the conditions have become a bit drier with the increase in evaporative minerals. In general, relatively low water and dry periods can be identified by increasing the rate of evaporation and expansion of dune sands and high water and wet periods can be observed by increasing the amount of debris sands as well as kaolinite and montmorillonite minerals.

The climate of Gavkhoni region in the post-Pleistocene has been wetter than today. This situation has continued for the past 18000 years and has led to the precession of shore lines. Since then, the early Holocene (past 12000 years) has been marked by a gradual decline in coastal humidity and backwash. With the onset of the Holocene, dry conditions gradually developed and peaked about 8000 years ago. In the middle and late Holocene, suitable humidity conditions have been gradually created and a water environment has been formed in the northern part of Playa due to the entrance of Zayandeh rud river. This situation was dominated by semi-arid conditions about a thousand years ago, which was accompanied by an increase in the evaporative deposits.

Over the past decade, Maxent (maximum entropy) algorithm has been used extensively in natural resource studies, especially on topics related to animal habitat suitability, plant species distribution, and so in prediction of species presence potential areas. Since the presence of springs as a point feature at the watershed indicates the ability of the occurrence of a spring at that point, the Maxent model can be used to assess water resources potential of the watershed. Therefore, this study tries to evaluate the capability of Maxent algorithm to determine the potential areas for predicting and mapping presence of springs on the Kabgian karstic watershed.

The data used in this study are divided to two categories of variables including dependent variable as points of presence of the spring, and independent variable as environmental information layers, including: Geographical Aspect, Lineament Density, Vertical Distance to the Channel Network (VDCN), Topographic Wetness ( TWI), Topographic Roughness (TRI), Topographic Position (TPI), Stream Density, Stream Power, Slope, Real Surface Area(RSA), Digital Elevation Model (DEM), Slope Curvature, Fault Density, Vegetation (NDVI), Vector Ruggedness Measure (VRM), precipitation and lithology. All modifications, layer preparation, classification, analysis, and map extraction was done using ArcGIS® 10.5, PCI Geomatica® 2018, SAGA GIS, MaxEnt® 3.3.3, Google Earth Pro 9, and Excel 2016 software.

The results showed that the accuracy of the model was very good with AUC 82.7 % on the ROC curve. According to Jackknife test, among 17 environmental variables, DEM, VDCN, rainfall, TPI and NDVI indices had the most impact on modeling, respectively. The least impact on modeling is VRM, geographical aspect, curvature, fault density, and lineament, respectively. The impact of the lithology index for predicting has been moderate; this can be attributed to the fact that more than 73% of the watershed is covered with limestone and gypsum which has a relatively uniform effect in modeling; This condition can also be seen in the lithology response curve which has given a significant response for Asmari and Gachsaran Formations. In general, the response curves of the most influential variables in the modeling were interpreted as follows: DEM response, first, increases in the range of 2200-2000 meters, then decreases to 2700 meters and again increases to 3700 meters. By moving away from the valley, the effect of the VDCN index is reversed; In other words, most springs have appeared near the valley. Areas with 1000 mm (and more) rainfall have been the most prone part of the basin. TPI index shows that with increasing basin surface bulge and slope, the areas prone to the presence of the spring decreases. The response of the NDVI index is positive and relatively uniform. The stream density response diagram has two significant breaks; it first decreases sharply at a density of 0.5 (km / km2) and increases sharply at a density of 1 to 1.3 (km / km2), then decreases again. The impact of watershed lithology in the area of Asmari and Gachsaran formations has been positive due to karstification and significant outcrop, and has had a negative response for other formations, especially Gurpi and Pabdeh formation due to being fine-grained. In general, the breaks in response curves are likely to be due to the interaction of other variables. The turning points of these curves and the range between these points, however, are the most effective part of the index that indicates whether the area is prone or not. According to the water resources suitability map, the areas with high, medium and low potential of the spring presence are 1894, 21795 and 63637 hectares, respectively. Areas with high potential are mainly located on the heights of Asmari and Gachsaran formations.

Between the 17 environmental indices used, none had acceptable predictive ability alone; this can be attributed to the complexity of karst systems and the difficulty of modeling it. The accuracy of the model was 82.7%, indicating the very good ability of the model in achieving the research goal. The final map of the areas prone to the presence of the spring is divided into three classes: high, medium and low, which include 2.2, 25 and 72.8 percent of the basin, respectively. Maxnet software, based on Shannon's entropy maximum, only needs the presence information of dependent variable and the information layers of the independent environmental variables to predict the most suitable regions for the presence of a dependent variable. Hence, the main advantage of this model is the accurate classification of potentials using layers of the presence of dependent variables; therefore, the possible error resulting from the absence or absence of points does not occur using this model. In overall, Shannon maximum entropy algorithm has a significant ability to determine the potential of groundwater resources in different areas and can be used for predicting and mapping karst water resources potential in watersheds.

 In 2001 and 2002, during a sudden rainfall, the area in question experienced floods with irreparable flow of debris. . the tangrah Catchment in Golestan Province is one of Dough River sub- basins. occurrence and Storm rain falls in 2001-2002, triggered a severe debris flow which left many casualties and economic losses. One of the important causes of sediment flow rock deposits is slippery masses that occur in the basin. Therefore, identifying areas that are more vulnerable to debris movements can be effective in planning to deal with reducing the effects of these events. In the present study, for a more detailed analysis, a landslide hazard map has been prepared using a random forest algorithm in this area. Factors affecting the occurrence of landslides in the strait area in relation to the debris flow, including parameters such as slope, slope direction, altitude, curvature amplitude, lithology, total annual rainfall, land use, distance from waterway, distance from fault and distance from Are ways of communication. Using Mat LAB R2020a software, 70% of this data was randomly selected for training and the rest was used for validation. Evaluation of the results obtained from the model of random forest algorithm has been created using a coefficient of determination of 0.88 and an error of 0.27. Prioritization of input factors by the algorithm indicates the greater importance of the factors of slope, height and distance from the road in the final forecast. The secondary validation is by the ROC criterion and the area below the curve is 0.93, which indicates the estimated accuracy of the created model. Based on the zoning, the results show that 16, 15, 11, 17 and 40% of the area are in very low, low, medium, high and very high classes, respectively. 

Debris flow is one of the most common geological hazards, often starting with landslides, and the potential energy of a slippery mass can be rapidly converted into kinetic energy. Deposits can lead to catastrophes that pose a serious threat to the lives and property of individuals and to economic development. Landslide is an integrated and often rapid volume of sedimentary material along slopes (Mahmoudi, 2007: 38). According to the definition of the Engineering Geological Society, landslide is the movement of a mass of material on a downward slope (Nasiri, 1383: 3). Therefore, these phenomena are one of the most important natural hazards that have serious casualties and financial losses around the world, especially in mountainous basins (Vilanova et al., 2010: 383) Landslides are of two types: Landslides in about one Meters is called shallow and a few meters deep. Shallow landslides contain a lot of water that occurs after heavy rainfall, but deep landslides require a longer time. When the earth's water level rises enough, the earth's block becomes unstable, causing landslides that often occur after the heaviest rains. Hence it will be a mechanism for creating a deposit (Takahashi, 124: 2007). The debris flow is actually the motion of old and new slips, and the potential energy of the slippery mass can be quickly converted into kinetic energy. Material flows can lead to catastrophes that pose a serious threat to the lives and property of individuals and to economic development. Many countries suffer from the serious risks of a deposit flow (Liang et al., 2012: 95).

Random forest is a modern type of rootstock that includes a host of classification and regression trees. The RF predictor model is based on averaging the results of all relevant decision trees and performs high-accuracy classification for many sets (Ebrahim Khani et al., 2011). The most important feature of random forest is its high performance in measuring the importance of a variable to determine what role each variable plays in predicting the response. The research has been prepared to prepare a map of sensitivity to debris flows and landslides in which 10 effective factors in amplitude instability have been used. After preparing 10 independent factors and landslide data, the coding of random forest algorithm in Mat LAB R2020a environment was used.

To determine the appropriate number of trees, using the mean square error (MSE) criterion, first some initial values for the number of trees were determined and then the model was implemented. By examining the mean error rate and the coefficient of determination for training, the optimal model with the lowest error rate was designed. The obtained model with a coefficient of determination of 0.88 and the square of the mean squared error was 0.27 for the training stage. In order to achieve a logical and appropriate model for the strait catchment, the ROC curve has been used. ROC curvature is one of the most complete methods in providing feature determination, probabilistic identification and prediction of systems, which slightly reduces the accuracy of the model. In this rock curve evaluation, the higher the rock curve surface, the higher the model accuracy. Studies have shown that factors such as slope, height and distance from the road have a very effective role in creating landslides in the region and factors such as distance from the waterway and distance from the fault and the direction of the slope have the least impact on landslides, respectively. slope factor is the first factor affecting instability in the region. Most debris flows originate from discrete or distributed source areas where slopes steeper than 30 ° are covered by debris or soils with low adhesion.

The results of the present study are consistent with the results of researchers such as Pour Ghasemi et al. (2015) in Mazandaran and Mohammadi et al. (2017), in a part of Golestan and Talebi et al. (2016) in Sardarabad watershed, Golestan province .

This research was conducted to prepare landslide susceptibility zonation (LSZ) map for the Oghan basin using Frequency Ratio(FR) and Statistical Index (SI) models. For this purpose, the most important factors affecting land sliding including slope, aspect, plane curvature, profile curvature, elevation, TWI, precipitation, land use, lithology, the distance from fault, the distance from drainage, the distance to road were determined. Then, the landslide inventory map was prepared by using field digital checks, GPS and Google earth. ROC curve and Area under the curve (AUC) were used to validation. research findings show that statistical index model for training data and validation are attributed as 0.925 and 0.916, respectively and they are more effective than frequency ratio model to prepare landslide susceptibility map, according to which 34.91, 28.51, and 36.59 % of basin area and 5.42, 20.46, and 35.72 % of population of the province encounter very low, low, moderate, high and very high risk of landslide. Also, geological and precipitation factors were introduced as the most important factors in the occurrence of landslides in the study area. 

Landslides are natural geological processes that change and reform Earth's surface relief. Landslides occur directly due to gravity and it can cause rapid and considerable movement of scree, soil, and rocks down the slopes. Knowing the landslide mechanism and zoning the landslide susceptible areas is necessary for land use planning. Obviously, preparing landslide susceptibility map (LSM) can give insight to land use managers and decision makers. Oghan watershed is located in Northern Iran, with geographical position between of 37ᵒ 9ʹ to 37ᵒ 15ʹ northern latitude and 55ᵒ 5ʹ to 43ʹ 55ᵒ Eastern longitude and it covers an area of 40352 Hectares. An important feature of this area is its height difference which varies between 191m to 2500m from sea level. Because of heavy rain and landslide prone formations, the area is potentially prone to mass wasting. As in points with high percentage slope and areas influenced by such human factors as road and land use changes, mass wasting is aggravated.

To identify landslides in the area under study, field visits, aerial photography, and Google Earth program were used, as a result of which 143 landslides were identified and their positions were spotted in Google earth and the final spot layer of landslides was produced in the form of shapefile. Finally, to conduct the research and execute models, 70 % of landslides (100 locations) were used as model training and the other 30% (43 landslide locations) for the validation. 12 factors affecting slope instability in the region under study were considered which include: elevation, slope, Aspect, profile curvature and plan curvature, lithology, distance from fault, land use, percipitation, distance from drainage, topographic wetness index, and distance from road. To draw susceptibility map for Oghan region, two models of frequency ratio and statistical index were considered and ROC curve was used for their comparison and validation. Frequency ratio (FR) refers to the ratio of landslide surface area in each class to the surface area of the related class. In fact, frequency ratio indicates little relationship between landslide occurrence and different variables affecting it. Statistical index model is a two-variable statistical analysis where the weight for each class factor affecting landslide occurrence is obtained by natural logarithm of landslide density of that class factors affecting landslide density of the whole region.

Most landslides occurred in heights of 168 to 1600m. Many villages, with approximate population of 9656 are in Oghan area, most of which are located in heights of 1600m. Further, most landslides are found in road sides and high traffic communication networks or near canal networks. This proves that shear stresses contribute to the landslide occurrence and, therefore, are used as effective factor in susceptibility assessment. Most landslides in the study area occur in slopes over 15%, in the East, South East, South, South West, in heights of 168 to 1200m, within surface curvature and Convex profile curvature. Moreover, TWI values smaller than 1.5 and larger than 4 are significant. Lar and Ilikah formations indicate the highest susceptibility. Distances between 1000-3000m for faults, smaller than 250m for drainage, and smaller than 500m or 3500-4000m for roads should be considered.

Comparison of maps produced by the given methods indicates that in frequency ratio method, 69% of Oghan watershed area (most of the area) is located in very low and low risk zone which houses 21.6 of the whole population, and the rest of the area is located in moderate to very high risk zone. In statistical index model, 34.91% of the watershed area is located in very low and low risk zone and most part of the area is located in moderate to very high risk zone and 5.42% of the population of the watershed live in very low and low risk zone, while most of the population reside in moderate, high and very high risk zones. Validation results show that the area under ROC curve for training data and validation in statistical index model is a little bit larger, excellent; and this proves that statistical index method has higher confidence coefficient and has relatively better performance than frequency ratio method.

 Rivers undergo many changes over time caused by various factors, such as geological, hydrological, and geomorphological features. One of the key topics in river engineering is the identification of river patterns (i.e. morphology). The study of the morphological aspects of rivers has always attracted the attention of specialists. Meandering rivers are among the most common types of rivers in nature with a linear form and very complex currents. The identification of these currents, which leads to the prediction of geometric pattern changes to rivers, requires sophisticated research tools. Fractal and multi-fractal analyses are among the most powerful analysis methods used in nonlinear systems to investigate complex patterns. This paper aims to investigate the Qara Aghaj River drainage basin in Fars province based on the multi-fractal geometry analysis of the waterway evolution process as well as the adaptive comparison derived from the regression operation of the multi-fractal parameters of the drainage basin with the central angle index of (A).

The Qara Aghaj River drainage basin is located on the eastern slopes of the Zagros Mountains between longitude of 47-51 and 14-54 N and latitude of 22-28 to 29-54 E. This drainage basin has an area of 13,050 km2. The drainage basin is bounded by the Kor River drainage basin as well as the Bakhtegan and Maharloo Lake drainage basin in the north, by the Kol River drainage basin in the east, by the Mand River drainage basin in the south, and by the Shapur and Dalaki River drainage basin in the west. In the fractal study of the Qara Aghaj River drainage basin in the Fars province, taking into account 3 study periods of the river, very high-quality satellite images were captured in those periods. ArcGIS software was used to perform image correction and process operations. After performing advanced image processing operations to extract the water pattern of the river in ArcGIS software, the river water pattern’s polygon file for all 3 study periods was transferred to AutoCAD software to measure the central angle morphology index (A). To determine the size of the central angle index, arcs were fit to each of the meanders with high precision in AutoCAD software for periods 1 to 3 of the river. Each of the meanders in the river is actually a river arc, with the successive river arcs being the focal point of this research for examining their evolution.

After checking the numerical value of each arc in the periods, given the numerical mean of the central angle of 115° for period 1 of the river, 108° for period 2, and 98° for period 3, which were all located within the numerical range of 85° and 158° (158> 115> 108> 98> 85), it was found out all the three selected river periods were of the developed meandering type. However, in the present study, the evolution pattern of the river was evaluated to examine the multi-fractal parameters. Accordingly, period 1 of the river included 22 evolution patterns, period 2 of the river included 23 evolution patterns, and period 3 of the river included 17 evolution patterns. According to the evolution trend of the calculated values, each morphological index of the central angle (A) for periods 1 to 3 of the river was calculated based on the average of its previous values, for each of the evolution patterns. In the present study, Fractalyse software was used to calculate each evolution pattern of periods 1 to 3 of the river. Upon importing the *.tiff image file of each evolution pattern for periods 1 to 3 of the river and using the box-counting method for calculating multi-fractal parameters, all analyzed numbers were exported in the form of *.txt files. Next, they were used for drawing diagrams and correcting some output errors of models in Microsoft Excel.

In the present study, given the study of the river evolution patterns for finding logical relationships between the central angle morphology index (A) and multi-fractal parameters, we deal with a lot of numbers in each pattern. Thus, the review of this subject was not possible without drawing a diagram, given the huge amount of data, all of which consisted of numbers and figures. Therefore, to examine more accurately and to convert all these numbers into a comparable criterion, multivariate regression analysis and the R2 regression coefficient were used for each diagram. To this end, for each of periods 1 to 3 of the river, the value of R2 was determined by drawing the diagram of the central angle in the order of the evolutionary pattern of successive arcs. Next, to create a certain weight ratio through multiplying the values of the central angles by each of the 15 corresponding multi-fractal parameters, the new value of R2 was determined.

The results obtained from running 180 regression diagram models in Excel software indicate that the evolution patterns of the river are affected by multi-fractal features. Parameters, such as 〖∆,D〗_f,〖Dq〗_((2) ),〖τq〗_((o) ),〖Dq〗_((max) ),〖Dq〗_((min) ), as well as α_((max) ), most of which being related to generalized and fractal dimensions, had the highest percentage of effectiveness in increasing the numerical value of the R2 regression coefficient in all periods 1 to 3 of the river. With a more detailed review, it was finally concluded that in the evolution pattern of period 1 of the river which had a more regular rhythm of successive arcs than the other two periods, i.e. periods 2 and 3, significant differences were produced in the results. This indicates that the central angles of the arcs in the river are always of great importance, and that they are very effective in the way the arcs are formed. In the end, we always witness how carefully the nature puts such angles and distances together, thereby creating a special order in the pattern of the complex mathematics of these natural phenomena.

Simulation of the effect of morphology change at the confluence of the drainage network on the erosion and sedimentation pattern of the Siminehrood River in Hamedan using the Fluent numerical model Abstract (extended) Because of hydraulic complexities, it has not been plausible to examine the confluence of river systems factoring in the laboratory limitations and the lack of use of three-dimensional models. The objective of this research is to simulate the impact of site morphology change on the erosion and sedimentation pattern of Siminehrood in Hamedan using the Fluent numerical model. The results pointed out that owing to the deviation of the current on the left bank, at first, micro-vortices form on the right bank, and with a larger size at the end of the stream. Besides, the Reynolds 130 stress study confirms that changing and increasing the flow velocity causes micro-vortices to form more rapidly and intensely on the right bank, and increases sedimentation on the left bank of the river. Continuation of this process changes the pattern of erosion and sedimentation and transforms the river into the form of arteries, prompts its deviation, and meandering. 

Since the use of physical models demands a large space, high expense, and a long time, several river engineering problems are studied with mathematical models (Azizi et al., 2019). In this respect, the use of the fluent mathematical model with minimal field information and computational volume has been extensively used in studies of bed change and river organization (Yasi et al., 2017). Kalami et al. (2019) assessed the geometric-hydraulic relationships of river cross-sections using the inverse solution of the Saint-Venant equations. The outcomes confirmed that hydraulic-hydrological routing methods have great accuracy in river flood simulation. Oda (2019), in modeling sediment transport and bed erosion and riverbank variations, revealed that the multiphase numerical model has a reliable performance in simulating sediment transport and erosion. Also, with this model, the limitations of experimental data can be mastered.

Siminehroud basin is the head of the main tributary of the Qarachachai river and surface and groundwater drainage of Hamedan-Bahar plains in Hamedan. In the present research, the outcomes of the simulation model of the flow and sediment pattern at the confluence of the canal from a narrow cross-section to a cross-section for an average yearly discharge of 5 cubic meters per second of the Simineh River from divergent to convergent and vice versa is used. To evaluate the accuracy of the Fluent model, water level profiles were predicted and longitudinal sedimentation and erosion profiles and the maximum sedimentation depth at the intersection at transverse sections were simulated by velocity-pressure evaluation. Also, using the Fluent numerical method, using the finite volume method, which is an accepted separation method and is efficient in solving the governing equations of the flow, the patterns of erosion and sediment transfer at the confluence of the two cross-sections of the river are discussed.

The simulation results prove that the formation of "erosive holes" (micro-vortices) first on the right bank causes the current to deviate to the left bank. This increases sedimentation on the left bank. Maintenance of such a process not only alters the manner and pattern of erosion and sedimentation in the bed and sides but also develops the river from a straight pattern to an arterial or alluvial pattern. Continuation of this process will ultimately result in the diversion and meandering of the river through the formation of sedimentary islands. Also, parallel with the increase of the distance from the watercourse inlet as the cross-sections change, the micro-vortices join more strongly and more rapidly, forming larger vortices on the left bank. Examination of Reynolds numbers also shows that at divergent-to-convergent junctions, most microbial sediments form on the right bank. Whereas, in rivers with convergent-divergent bedrocks, the creation of micro-vortices due to a swift increase in velocity and constant pressure in the narrowing of the bed, forms sedimentary ridges symmetrically on the left and right banks of the river.

At the confluence of the flow, the flow pattern is such that deep erosion holes in the bed, shore erosion, sedimentation, and finally strong vortices form. This, in itself, alters the morphology of the river. Studies show that at the junction of divergent-to-convergent currents, owing to variations and increases in flow velocities, micro-vortices form more rapidly and intensely on the right bank, with a bed less wide than the right bank. In rivers with divergent confluence, micro-vortices first form on the bank of a side of the river whose bed width is somewhat smaller than the opposite side. The constant formation of sediment ridges near the river yields the transformation of the river into an arterial form. Knowledge of how these micro-vortices are formed, by identifying the location and formation of sedimentary ridges on the river bank, will lead to the more successful design and implementation of river management plans.

Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

The purpose of present study is seismicity analysis of Lorestan folded arc and its adjacent thrust belt using quantitative methods. To reach this aim we performed analysis of seismicity using quantitative methods to find possible vertical and horizontal changes in seismic activity across the main Zagros faults of the northwestern part of Zagros. Firstly, we used fractal geometry and frequency-magnitude distribution of earthquakes by using FD and b-value parameters, respectively. Here b-value is the main factor in Gutenberg-Richter empirical relation which indicates the exponential distribution of earthquake magnitudes (Godano et al, 2014; 1765). This parameter also is known as fractal dimension (Mirabedini & aghatabay,2015: 60). FD is fractal dimension of earthquake epicenters distribution which has been calculated by box-counting method (Turcotte 1997). On the other hand Entropy model has been applied to specify potential of seismicity by using effective factors and 30 points of earthquake concentration. The study area in northwestern part of Zagros was divided to the simply folded arc of Lorestan and faulted-folded belt of high Zagros. Several main faults pass through the area from NW to SE and divide its main morphotectonic units as High, folded and foredeep parts of Zagros (Berberian, 1995: 193).

Data in this research can be divided to two part: parameters of earthquakes (magnitude, depth, location of epicenter) and linear data of faults and anticline/syncline axes. These data have been changed into new layers by GIS software extensions (density of epicenter and depth of earthquakes, density of faults and anticline/syncline axes, distance of fault and epicenter of earthquakes, interpolation of epicenter of earthquakes) to be applied in Entropy model, in other hand frequency of magnitude clusters and surface distribution of earthquakes are main data in Gutenberg–Richter relation and Fractal methods respectively. Numerical results of mentioned methods have been calculated and drawn in excel software. Gutenberg–Richter relation (Gutenberg & Richter 1944) is defined as Log N(m)= a-bm, where N is the cumulative number of earthquakes with magnitude larger or equal to m, a is a constant (seismicity level) and b is the slope of frequency-magnitude (size distribution) (Godano, 2014). To calculate fractal dimension of distribution of earthquake epicenters, box counting method suggested by Turcotte (1997) were applied by using Hausdorff dimension, which in two quantity of size (side length of grids) and number (number of grid boxes containing earthquake) are used to calculate FD value (Schuller et al, 2001: 3). In the other section, earthquake epicenters are divided to several clusters with different magnitude, then kernel density of each cluster was applied and subsequently, the maximum concentration of each magnitude cluster was determined as a point layer. Followingly, by overlaying these point layer with effective layers in seismicity analysis, their characteristics was extracted. Finally, an Entropy matrix was calculated and using experts rating and computing the layer’s weight, seismic zones were identified (Zonggi, et al, 2010).

Estimated b-value indicates approximately reciprocal values compared with FD values. Decrease in b-value reveals that stress level and probability of large magnitude earthquakes occurrence is quite high and increase in FD shows that earthquakes are not clustered and are distributed homogeneously along a line in understudy area. Calculated number-size values for earthquakes represent both partial and popular FD changes. Based on partial FD, three populations can be classified: (a) Background with FD larger than popular FD; (b) Threshold with FD lower than 0.7: and (c) Anomaly with FD more than two. Based on popular FD, distribution of earthquakes is linear and transition to chaos phase is not predicted. Comparison between maximum values of Entropy zoning and FD values for each box indicates that these two values show 93% correlation (regardless of the C box values due to incompatibility with value of other boxes).

Areas with high FD value and low b-value are more tectonically active. The box labeled A which represent western parts of Kermanshah in folded Zagros, has the highest FD value (1.02) and lowest b-value (0.78). The box labeled F in southern east part is in contrast with it (highest b value:1.02 and one of the lowest FD value: 0.89) in understudy areas. E (Balarud fault) and D (High and folded Zagros) parts have almost the same FD and b values. FD and b values in B (high Zagros) are equal and less than the aforementioned areas. C (that contains a part of mountain front fault) has the lowest value of FD and same b-value as B and the changes of Entropy max values are same as FD values.

The Leilan alluvial fan in the northwest of Iran has been exploited by humans due to its topographical, geological and climatic conditions and has established many cities (Leilan and Miandoab) and many villages on its surface. Over-use of alluvial fan capacity by its residents over many years has made the alluvial fan vulnerable to geomorphic hazards. The most important geomorphic hazards that threaten the alluvial fan are flood risk. The development of agriculture on the alluvial fan surface and river encroachment has increased the risk of flooding on the margins and floodplains of the Leilan Chai River. The flooding of the Leilan River in the spring causes a lot of damage to the farmland around the river. The Leilan alluvial fan is also at risk of flooding the Leilan Chai Seasonal River. The purpose of this study was to evaluate the flood risk dynamics at the surface of this alluvial fan. Therefore, the flood hazard assessment of the Leilan Chai River with a length of 15.4 km was carried out using HEC-RAS hydraulic model.

The study area is located in thewestern and eastern Azerbaijan provinces, and the cities of Leilan and part of the Miandoab city are located on it. This alluvial fan is located between 36o 55′ 10″ to 37o 00′ 40″ N and 46o 06′ 17″ to 46o 17′ 17″ E. The following data, software, and methods were used to dynamically analysis flood risk and prepare flood risk maps with different return periods in the study area: - Landsat 8 OLI satellite image for 2019 (path and row 168-134). - Topographic maps 1: 20000 and 1: 25000 of the study area. - Digital Elevation Model (DEM) with 12.5 m spatial resolution. - Shirin Kand hydrometer station data including daily and monthly discharge. - HEC-RAS hydrodynamic software for simulating flood in alluvial fan surface. - HEC-GeoRAS extension to provide the required parameters for the HEC-RAS model. - ENVI software for land use mapping. - ArcGIS software to generate maps.

HEC-GeoRAS extension was used in ArcGIS software to determine the geometric characteristics of the Leilan Chai River and The layers required for model implementation such as centerline, river banks (right and left bank), flood plain, cross-sections, bridges, and floodgates were prepared. Due to the floodgate on the river to perform flood simulation, the Leilan Chai River is divided into two reachs (upstream and downstream of the floodgate). In this research, after field surveys and gathering the required information, 151 cross-sections with a distance of 100 m and a width of 1000 m were created on Leilan Chai in ArcGIS software. It was also found in field surveys that in the second reach most of the rivers were occupied by humans and converted to agricultural land and as a result, the river width in this area was significantly lower than the first. Land use map and field studies were used to determine the coefficient of roughness in the bed and shores of the Leilan Chai river.

The present study, using the HEC-RAS hydrodynamic model, simulates the Leilan chai River flow on the Leilan alluvial fan during different return periods. The peak daily discharge data recorded at Shirin Kand hydrometer station were used to predict peak discharge values. Study of the flood maps show that flood risk does not pose a risk of flooding during the 2 and 5 year return periods of agricultural lands and residential areas around the river. But in return periods of 10 years or more, we see flooding of agricultural lands and residential areas around Leilan Chai. During the 25-year return period, 120.2 hectares of agricultural land and about 9.1 hectares of residential areas along the river are affected by the risk of flooding. The village of Mullah Shahabuddin is one of the areas developed along the river and as a result is at risk of flooding in the event of heavy rainfall. The most vulnerable areas are in the second reach due to the occupancy of the river bed by residents of the region and converting their marginal land into agricultural land and residential areas. Therefore, it is necessary to carry out a serious review of the riverbed in order to prevent accidents and the possible financial and casualties in the event of flooding in the Leilan Chai River, and that the river's privacy should not be further confiscated. It was also found in field surveys that in the second reach most of the rivers were occupied by humans and converted to agricultural land and as a result, the river width in this area was significantly lower than the first. Land use map and field studies were used to determine the coefficient of roughness in the bed and shores of the Leilan Chai river.

Given Iran's ongoing role in the Middle East and the constant threat of external threats, it is imperative that I take action on the critical and important steps of my country Of the measures that can prevent the occurrence of malignancies Choose the right place is the activity in which the ability of a particular region, the existence of appropriate and sufficient land and its consistency with other urban and rural land uses is analyzed to select suitable locations for the desired application. Among these, site appropriates selection analysis for Select critical, critical and important centers with effective conditions and factors in the selection is different from the analysis for trade and industry. In the process of appropriates selection some factors including the right location should take some factors including geomorphological parameters mission, type and size of the units and the natural features of the area must be considered. Since the initial studies to determine the suitable site Presidio as well as the construction expenses, it costs very huge and in terms of security it is very important. Thus, it requires using of appropriate methods in the process of site selection Presidio. Reducing costs and making the wrong decisions and increasing efficiency and performance in selecting critical centersA proper analysis can also prevent dissipation of funds and time. So to the importance of scientific research in site appropriates selection critical and sensitive geomorphologic assessment centers in Kermanshah province with 33 °, 40 'to 35 °, 18' N and 45 ° geographic location.

In locating critical and sensitive centers using geomorphological parameters, this research has used questionnaire design through survey studies and interviews with geomorphological experts about factors affecting the selection of critical centers. Then ratings based on the weights of criteria for each factor was identified. Finally, data layers of region such as slope map, slope direction, elevation, line maps, distance from urban and rural centers, Distance from the river, fault maps, geological maps(1:100000) Climatic Parameters , Aerial photo with scale(1/55000) , Landsat satellite imagery ETM, TM, MMS and Google Earth are used to identify the landforms point addresses have been collected and converted into rasters and multiplied by the weights of the criteria, the suitable locations have been chosen. In this study, IRDAS IMAGING Software for image processing of satellite data, Expert Choice software for the analysis of hierarchical and weighted criteria, Arc GIS software for editing, layer preparation, analysis and final site selection and field studies for complying with the maps of the area and visits of existing military centers have been used to get final results

In this study, factors affecting the selection of critical and sensitive centers were prioritized and then appropriate weight for each parameter was considered. The overall rating for each factor has been obtained by multiplying the response of each rating in table valuation factors considering their coefficients. The sum has been calculated and by dividing the total points by the total number of questionnaires the final score has been revealed for each factor Maps and base imagery have been collected and geo-referenced, sectioned in time scale and the layers required to form a thematic map have been prepared in vector format. Then all layers for comparison and involvement in decision making have been plugged in Raster Calculator from Spatial Analyst Tool. This give a raster of suitability for the area. The final map is classified into five categories including Very Good and Good (Dark Color), Medium (Half Tone) and Inappropriate Very Inappropriate (Light Colors).

In this research, the research tools are surveyed topographic maps, geology and satellite and aerial images of the area as well as interviews with experts identifying the effective factors in locating critical and important centers with emphasis on passive defense..And after identifying them using AHP (Multi Criteria Decision Making Models) in order to compare the criteria has been analyzed. Also according to the final map based on geomorphological factors in ARC- GIS software environment The area of optimum area for the location of critical and sensitive areas in the northern part is greater than the soothe In other words, Given the zoning we can saycan be said that Kermanshah province has more favorable and more favorable conditions in the north, northwest and south areas, and is in a more favorable condition, and the west and east areas have poor conditions.Today, using digital and elevation models and Earth science software can easily identify areas that have the most visibility and dominance around them.

Erosion of catchments and sediment load of rivers is one of the serious challenges of water resources management in the country, which has negative consequences in the operation of water facilities and dams. On the other hand, the amount of runoff, erosion and sediment transport varies depending on the hydrological conditions, soil and cover in the basin and this makes the simulation of the above processes need to provide the necessary information on how these factors change spatially (Jirani et al., 2011). Due to the diversity of the topography of the country, the majority of the watershed, especially in mountainous areas impassable, lack of gauging stations are sufficient and because the data these stations in different parts water resources management is used to simulate phenomena hydrology of the basin The optimal solution for this is data loss (Rostamian, 2006). Many models have been proposed for the description and forecast of the hydrology of the catchment, which is very different in terms of purpose, scale, time, and place (Sitgen et al., 2008).

SAWT is a physical model and instead of using regression equations to describe the relationship between input and output variables, it receives specific information about climate, soil, topography, vegetation, and land cover in the catchment. The physical processes related to water movement, sediment movement, plant growth, nutrient cycle in this model are simulated directly from the input parameters. The advantages of this method are that: 1- Basins without collected data (flow measurement information) can also be simulated. 2- The relative effect of input information (change in management methods, climate, and vegetation) on water quality and other variables can be quantified. The SWAT model uses easy and accessible input parameters and is very computationally efficient. Large and complex simulations, with different management strategies that can be implemented without spending a lot of time and money, enable the user to study the long-term effects. The subdivision is subdivided according to the characteristics of surface cover, land management, and soil characteristics. Calculations in the SWAT model to determine HRUs are performed using Equation (1) (Zahbiun et al., 2010). Equation (1): SWt = SW0 + – Qsurf – Ea – Wseep – Qgw) SWt = final amount of soil water (mm) SW0 = initial amount of soil water on the day I (mm) t = time (daily) Rday = Precipitation on the 1st day (mm) Qsurf = amount of surface runoff on the day I (mm) Ea = amount of evapotranspiration on the day I (mm) Wseep = amount of water entering from the unsaturated zone in soil profile on the day I (mm) Qgw = is the amount of return current on the day I (mm).

For Latian catchment, DEM with a spatial resolution of 12.5 m extracted from ALOS PALSAR satellite imagery, vector layer of land use, and soil texture of Lavasanat region with appropriate accuracy and Lavizan and Shimran synoptic stations were used. According to the land use map in the region, there are 4 different uses including rangeland, residential area, garden and lake, which due to the existence of two different soil classes in the region and three slope classes from 0 to 40, 40 to 60 and more than 60 degrees sub-basins and Hydrological response units were extracted in the area, which includes 34 sub-basins and 206 hydrological response units (HRU). According to the obtained model, the amount of surface runoff in the basin is equal to 67.06 mm and the amount of evapotranspiration in the desired catchment is equal to 117.7 mm and the average amount of sediment produced in the basin is equal to 1240.41 mg/ha and the maximum amount of sediment produced in the catchment is 3369.93 mg/ha, of which 1231.65 mg/ha is deposited downstream of the basin.

In this study, using the SWAT model, the hydrological characteristics of Latian catchment were investigated. Based on the results obtained in the region, there are 4 different uses including rangeland, residential area, garden, and lake, which due to the existence of two different soil classes in the region. And three slope classes of 0 to 40, 40 to 60 and more than 60 degrees of sub-basins and hydrological response units of the region were extracted, which include 34 sub-basins and 206 hydrological response units (HRU). After determining the hydrological response units of the basin using daily synoptic data, the CN value of the basin is 82.1, which according to the definition of CN means that out of 395.9 mm of annual rainfall, the soil of this basin is only able to absorb 17.9% and The rest of the precipitation on the surface is in the form of surface runoff and evapotranspiration, which is a relatively high amount. Infiltrates and makes the catchment flood-prone to occur, the construction of the Latian Dam downstream of the basin will control floods from above. According to the model results, the amount of surface runoff in the basin is 67.06 mm and the amount of evapotranspiration in the catchment is 117.7 mm. Also, the average amount of sediment produced in the basin is equal to 1240.41 mg/ha and the maximum amount of sediment produced in the catchment is 3369.93 mg/ha, of which 1231.65 mg/ha is deposited downstream of the basin, so water erosion and sediment production in Latian catchment This can have adverse effects on the Latian Dam. According to the obtained results and determining the average amount of runoff and sediment entering the dam, the dam conditions in sudden rains and prevented possible hazards.

Many studies have been done in different parts of the world on basins and waterways using morphometric indices that indicating their capability to identify active areas. Morphotectonic analysis is a useful tool for identifying forms created on earth by tectonic processes. Tectonics Geomorphology is the knowledge of the study of shapes and forms created on the ground by tectonic mechanisms. The geometry of the rivers network can be described qualitatively and quantitatively in a number of ways. The use of morphometric indices to evaluate tectonic activity was started by (Horton, 1945) and followed by other researchers such as (Stahrler, 1952) and (Bull & McFadden, 1977) and has continued to this day. The study area is Saadat Abad basin that is located in south of Iran and in Fars province and it is one of the sub-basins of Bakhtegan-Maharloo catchment. The study area is located in the high Zagros range. There are several faults in the region that including the Sivand River, Sivand, Musaikhani, and Avenjan faults. Since the relationship between active tectonics and geomorphology in Saadat Abad basin has not been studied so far, therefore, it is necessary to investigate the morphometry in order to identify the effect of active tectonics on the tectonic evolution of drainage basins and rivers in this area. In this study, Active tectonic in Saadat Abad basin using five geomorphological indices of the Drainage Asymmetry Factor (Af), Relative relief (Bh), Form factor (Ff), Hypsometric integral and curve (Hi), and Stream gradient index (SL) have been assessed and Finally a final index called Recent relative active tectonic index (IAT) was calculated.

Using the Digital Elevation Model (DEM), the characteristics and indicators of the river can be accurately extracted and analyzed the drainage basin. In this study, in order to determine active tectonics using morphometric indices in the study area, at first, using the digital elevation map (DEM) with a horizontal resolution of 30 m in geographic information systems (GIS) and by using one of the GIS software extensions called Arc Hydro, sub-basins and rivers of the study area were extracted and the study area was divided into 21 sub-basins. Then we calculated the index for each sub-basin separately. In the next step, Using geological maps with scale of 1: 100,000 and 1: 250000 Geological Survey of Iran in the study area, the geological units and major structures of the region, including faults were determined and for each index the zoning map of tectonic activity in the study area was plotted.

In this section, results of the index measurements with the main structures of the area were analyzed. Each index was divided into three categories in terms of tectonic activity: Class 1 (high relative tectonic activity), Class 2 (moderate tectonic activity) and Class 3 (low tectonic activity). In the last step, in order to determine the level of total tectonic activity, a relative active tectonic index (Iat) was calculated and the zoning map of this index was plotted. Drainage Asymmetry Factor (AF) shows tectonic tilt in drainage basin. Structural factors, such as rock layering, may play an important role in increasing the basin asymmetry index of the drainage basin. Relative relief index (Bh) indicates the relative height of the basin. This index is calculated from the difference between the highest and lowest points in the basin and the high rate of represents the high level of uplift that is because of active tectonics of area. According to the form factor index (Ff), the drainage basins are more elongated in geologically active areas. Therefore, basins with lower values of the form factor index are more active in terms of tectonics. The Hypsometric integral (Hi) describes the relative distribution of elevation in a drainage basin and the high rate of Relative relief index, represents the high level of uplift in the study area that is because of active tectonics. Stream gradient index (SL) shows the effect of environmental changes on river longitudinal profile.

According to the relative active tectonic index (Iat) and zoning map of this index which shows the total tectonic activity in the Saadat Abad Basin, Basins 1 and 5 due to the activity of the Sivand River fault, basin 6 due to the activity of the Avanjan fault and basin 8 due to the activity of the Musa-Khani fault, have a high recent relative tectonic activity in the area. Also basins 12 and 19, which are affected by faults activity that are located in the south of the region, have high tectonic activity.

The study area is located in the north of the Oman Sea and southeast of Iran in Sistan and Baluchestan province. The Makran Trench is the physiographic expression of a subduction zone along the northeastern margin of the Oman Sea adjacent to the southwestern coast of Baluchestan of Pakistan and the southeastern coast of Iran. Numerous geomorphological landscapes such as cliffs, Omega and U-shaped bays, erosion columns, various faults and so on have been formed in a beautiful and unique way in the region. In this region the oceanic crust of the Indian Ocean Plate is being subducted beneath the continental crust of the Makran Plate. Makran is one of the largest accretionary wedges on the globe. The study area between latitudes 25 ̊ to 25̊ and 45̍ north and longitude 56 ̊, 45̍ lengths up to 61̊, 52̍ north of the Sea of Oman and the north-eastern Sistan-Baluchestan province is located. The study area is located in the external part, the structure of the Makran coastal land area larger than the selected range. In the Makran region, the Arabian Plate subducts beneath the Eurasian Plate at ~4 cm/yr. This subduction is associated with an accretionary wedge of sediments which has developed since the Cenozoic To the west, the Makran Trench is connected by the Minab Fault system to the Zagros fold and thrust belt. The Makran accretionary complex is characterized by a number of features associated with escaping water and methane. Mud volcanoes are found onshore in both Iran and Pakistan, and cold seeps exist offshore for example Tang Mud volcano. The formation of an island (Zalzala Jazeera) after the 2013 Baluchestan earthquakes is thought to be the result of a mud volcano. Extreme coastal inundation associated with the 2004 Indian Ocean and 1945 Makran tsunamigenic–earthquakes highlight the risk of Tsunamis to coastlines of the northern Oman Sea (Vaziri et al., 2019).

In this research have been used the methods of Office and library studies, Field Studies (photographed and documented in structural geology features, 85 thin sections and 62 washed samples that were collected in the study areas), Laboratory studies [Thin sections were stained using Alizarin Red S (Dickson, 1966) and were studied using standard petrographic microscope techniques. Carbonate rocks were classified according to Dunham’s carbonate classification (1962), and siliciclastic rocks were classified using Folk’s classification (1980). At this point, a set of 85 samples, 62 thin sections were studied to determine the lithofacies. In each of the thin, skeletal and non-skeletal components were identified and the percentage of each constituent grains major and minor chart using comparative Flugel (2010), respectively. As well as 23 samples of sediments were paleontological studies. On the basis of petrographic studies classification of carbonate rocks by Dunham’s method and nomination of microfacies by Flugel’s classification was done and three basic microfacies have been identified. Based on the identification of different lithofacies and interpretation of their depositional environments. We applied the concepts that were developed by many workers to determine sequence boundaries, depositional sequences and system tracts] and Data analysis, interpretation and conclusions: The initial processing by computer skills such as Excel and final processing using software was computed. Sea level positions were interpreted for the Pleistocene interval based on lithofacies variations with the sequence stratigraphic framework established in this study.

The rock types are siltstone, sandstone, conglomerate and lumachelle and limestone. Strata of the Makran Formation are subdivided into two major carbonate lithofacies and two siliciclastic lithofacies. Based on skeletal grains and the amounts of micrite, the sediments deposited in the lithofacies of the sandy packstone to siltstone lithofacies were deposited in relatively deep water, below fair-weather wave base under low-energy environmental (Open-Marine) conditions. The presence bioclastic debris of stenohaline indicate that sandy grainstone were deposited in an upper shoreface setting. Plotting the interpreted relative water depths vs. Stratigraphic position for each occurrence of each subfacies shows a predictable stacking pattern that formed coarsening upward cycles. These meter-scale shallowing-upward cycles in the Pleistocene interval formed in response to sea level fluctuations coupled with subsidence due to both sediment loading and tectonic movements. Because of terrigenous influx, siliciclastic sandstone and siltstone were deposited in the dominantly carbonate-producing area, formed a mixed siliciclastic-carbonate sequence. At times when the siliciclastic point sources were shifted far to the south carbonate deposition reestablished itself in southeastern Iran.

Makran is a constant competition between tectonic processes and erosion processes, shaping the geomorphological changes of the region (such as erosion columns, folds, cliffs, bad land, Omega bays, sand dunes, dissolution cavities, taphoni cavities, coastal falls and so on). The presence of several mud volcano in Makran accretionary complex with special features in the coastal areas of Iran and Pakistan shows the effect of active tectonics on the geomorphology of the study area. Due to the increase in sediment thickness from west to east, increase in subduction rate of Makran zone, coastal uplift and faulting of coastal barracks and the entry of destructive sediments and the direction of old flow in sedimentary structures, it seems that the origin of clastic grains in the north It is a study that reduces the amount of detrital particles and creates conditions for the sedimentation of carbonates. Therefore, the coastal sedimentary model is proposed for Pleistocene sediments that have been deposited under dual conditions in terms of sedimentary environment energy. These geomorphological changes are caused by regional sea level changes, sedimentation process, the tectonic process caused by subduction of oceanic and continental plates, relative sea level changes and evidence-based climate change due to evidence-based glacial and interglacial periods.