ابراهیم بهشتی جاوید

During the last years, following an increase in damages due to occurrence of landslides, human has decided to reduce such losses. Therefore, identifying regions susceptible to landslide and classifying them can partly help man to prevent from happening above phenomena. The current study aims to demonstrate factors contributing to occurrence of landslide in Candrigh Chay basin and then, flattening the basin regarding risks related to landslide event using one methods, Network Analysis Process. In this direction, as respects to basin morphology and also, findings of past researches , ten factors involved in occurring landslides in Candrigh Chay Basin were recognized and used : lithology , land use , rainfall , slope , slope aspect , road slide , sluice power index (SPI), sediment transfer index (STI).Network Analysis Process(ANP) Method was implemented to score and classify factors and  scales. Was carried out in order to measure classes of each parameter. The final map showed that the basin has 4 classes considering landslide. Findings reveal flats with low risk and flats with high risk have the least area in Candrigh Chay basin. On the other hand, flats with medium risk and high risk allocate the most area of basin for themself. Comparing slid surfaces with flats facing risks indicates regions located at high and average risk class possess most areas of slide surfaces so that a flat involving very high risk  and a flat with high risk devote 35 percent (79km²) and 32/6 (72km²) of landslides for themselves, respectively. In other words, more than 77 percent of landslides conform to flats covering very high and average risk. Moreover, petrology, slope, and, sluice slide were identified as the most effective agents in occurring land_slides. One models, Network Analysis Process (ANP) were utilized through this project

Geomorphometry is the science of quantitative land-surface analysis (Pike, 1995, 2000a; Raseman et al., 2004). It is an interdisciplinary field that has evolved from mathematics, the Earth sciences, and most recently computer sciences (Pike et al, 2008, 3). It is well to keep in mind the two overarching modes of geomorphometric analysis first distinguished by Evans (1972) as specific, addressing discrete surface features (i.e. Landforms), and general, treating the continuous land surface. The morphometry of landforms per se, with or without the use of digital data, is more correctly considered part of the quantitative geomorphology (Thorn, 1988; Scheidegger, 1991; Leopold et al., 1995; Rhoads &Thorn, 1996). The shape of terrain, i.e. landforms, influences flow of surface water, transport of sediments, and soil production, and determines climate on local and regional scales. Furthermore, natural phenomena like vegetation are directly influenced by landform patterns and their relative position across the landscape (Blaszczynski 1997; Blaschke & Strobl, 2003).
The Earth’s surface is structured into landforms as a result of the cumulative influence of geomorphic, geological, hydrological, ecological, and soil forming processes that have acted on over time. Landforms define boundary conditions for processes operative in the fields of geomorphology, hydrology, ecology, pedology and others (Dikau, 1989; Dikau et al., 1995; Pike, 1995, 2000a; Dehn et al., 2001). In this study, using MRS algorithms and Ecognition software, landforms in the northern slopes of Mount Sabalan have been extracted and the effects of Landform morphometry on its hydrology have been investigated The semi-automated methods refer to the automatic procedures of extracting a landform based-process. This is mainly relying on unsupervised isodata classification, pixel-based classification (supervised /subpixel classifier based on training material), the analysis of digital elevation models (DEM), algorithms, hydrological modelling, and object oriented analysis (Nabil and Moawad, 2014:42).
In this study object-oriented methods and Ecognition software were used for the classification and the extraction of landforms. The object-oriented classification was used as an alternative to traditional pixel-based classifications, to cluster grid cells into homogeneous objects, which can be classified as geomorphological features (Seijmonsbergen, 2012). In addition, the DEM and its derivation (Slope, Profile and plan curvatures, maximum and minimum curvatures), were used in order to extract landforms. Then, using fuzzy logic method, the landform, land use, NDVI index , precipitation, density of river, and lithology layers were Overlaid and the potential flooding area was obtained. In the object-oriented method, determining the scale parameter is a very important factor in the separation of different objects in an image. Scale parameter is a crucial threshold that determines the maximum allowed heterogeneity for segmentation and has a direct influence on the size of the objects to be obtained. The scale parameter, after a trial and error process, is recognized to be within a particular range (Gerçek, 2010:115). A novel method that was introduced by Dragut et al. (2010) and the ‘Estimation of Scale Parameter (ESP) that built on the idea of ‘Local Variance’ (LV) were employed to obtain the optimum scale out of a range of scales. By interpreting thresholds and prominent peaks in the ROC-LV graph, characteristic scales relative to data properties at the scene level could be found. This curve in 100 scale level was produced for the study area by using the ESP software and with respect to curve, the scale of 25 was selected for the segmentation. After segmentation, using the morphometric differences between the landforms, the landforms were extracted. After this stage, the landforms along with three layers of NDVI index, land use, and lithology was fuzzy. Finally, using gamma 0.8, they were combined and the zoning map of the potential flooding was estimated. Flood zoning map was classified into 5 classes and the percentage of each zone risk was calculated in each landform. In this research, using an object-oriented model, landforms were extracted as plain, peak, pit, ridge, channel, nose, shoulder slope, hollow shoulder, spur, planar slope, hollow, spur foot slope, and hollow foot. An assessment of the effect of landforms on the hydrology of the area revealed that three landforms of hollow, shoulder and planar slope which were respectively 67.3%, 62.9%, and 53.2% had the greatest impact on flooding and their area were zoned as high and very high flooding. On the other hand, plain and pit landforms were zoned in the form of low and very low flooding areas.

Flood is largest and most important climate crisis that kills thousands of people every year. And causes many damages to human society and the environment. This phenomenon of the distant past has always been a fear in human society (Nasrin Nejad et all, 2014: 16). Flood is one of the three natural disasters and major events in Iran and to note that at least a year, a major flood occurred in the country. According to studies in each year number of 40 small and large floods occurs in parts of the country (Department of Planning And Strategic Supervision president, 2010). Today is considered Non-structural deal with floods in catchment. Accordingly, the first action that comes to reduce the threat of flooding is flood control in the upper basin (Abdi, 2006:200). One of the common methods in combating the floods is Identifying in terms of potential flooding in basin. zoning possibility of recognizing watershed Specifies in terms of the flood.
Several studies have been conducted in the field of flood inside and outside the country that among these can be noted in research conducted by Thilagavathi et all(2011), Ozturk et all(2013), nasrin Nejad et all (2014), eskandary nejad et all (2015) and etc. Determination of flooding and flood-prone zoning Lighvan River is the main objective of this research. Lighvan River originates from the northern slopes of Sahand and after passing through the Tabriz and Basmenj cities eventually pours into Urmia Lake.
Determination of flooding and flood-prone zoning Lighvan River is the main objective of this research. To do so has been used a combination of two Analytical Network Process and fuzzy logic models and along with 12 environmental parameters.
Analytical Network Process: Analytical Network Process model is broad form of AHP model, Analytical Hierarchy Process is introduced by the Saaty (1980). The underlying assumption of this method is Existence of independent sub-criteria together (Saaty, 2006). Saaty In cases where this principle is violated, and structure of issue has become a network, introduces Analytical Network Process model (Amalnik et al, 2010:202). Analytical Network Process for each subject considers in the form of a network of criteria, sub-criteria and options that have been gathered together in clusters. This model has several steps, which are as follows: 1) build models and create a network structure 2) binary comparison and determine the priority vectors - create a unweight super matrix 4) Creating weighted super matrix 5) finally, create a Limit super matrix.
Fuzzy logic model: Fuzzy logic model is a generalization of the classical set theory in mathematics, science, and every day is a new method to express uncertainty and ambiguity. Fuzzy sets are defined by membership functions. For each fuzzy set between zero and there is a lack of full membership and a full membership represents zero (Hosseini et al, 1390). Fuzzy modeling is done by using several operators. An important function of the fuzzy logic model can be fuzzy Algebraic multiplication operator (Fuzzy Product), sum Fuzzy (Fuzzy Sum) gamma phase and so on. This operator of multiplication by the algebraic sum of fuzzy based on the fuzzy relation (1) is defined. Equation (1)
The parameters used in the modeling, due to the nature of their were categorized in four clusters such as: hydroclimate, geomorphology, geology and land cover for potential zoning for the flooding and two clusters of geomorphology and hydroclimate for zoning. Continue on with taking the network connection between the clusters and the parameters, established network structure between the parameters. By forming network structure between the clusters and the parameters arrives stage implementation of Analytical Network Process model, the formation of super matrix's and paired comparison. Due to the existence of two goals in the research (flood and flooding potential zoning) and impact of different parameters in them, Analytical Network Process model was conducted for each of the goals individually and in view of Effect In the flooding and flood risk and their relative weights Was obtained to each other.
Based on Comparisons of currently criteria, slope (0.18), lithology (0.125), aspect (0.118) have highest impact factor in flooding and slope parameters (0.229), the plan curvature (0.2 ) and profile curvature (0.134) have the highest impact factor in the flood risk in the study basin.
According to this research results, the southern part of the basin have been introduced as the prone to flooding areas with classifying in zones with high and very high potential. This area due to volcanic bedrock, Low permeability, High slope, more rainfall and high drainage density Have the ability to produce high runoff And that is why are located in these classes. In terms of flood risk the northern half of the basin's the greatest potential for flood risk. These areas mainly include land surrounding the main River, low and flat lands portion of the basin output. In terms of subdivisions land units these regions, mainly are known as the land with low roughness and relative slope less than 10% that included marginal areas of rivers, old and new terraces And have been characterized With features such as low slope, low relative height, low distance from river and the concave plan and profiles curvature.

Introduction Semi-automated extraction of selected landform types from land-surface models such as digital elevation models (DEMs), curvatures, and slope gradients is of particular interest in geomorphology, hydrology and related fields(Eisank et al,2010:1). Geomorphometry is the science of quantitative land-surface analysis (Pike, 1995, 2000a; Rasemann et al., 2004). The morphometry of landforms perse, by or without the use of digital data, is more correctly considered part of quantitative geomorphology (Thorn, 1988; Scheidegger, 1991 Leopold et al., 1995; Rhoads and Thorn, 1996).
Evans (1972) was the first person that divided Geomorphometry in two branches the general and special Geomorphometry, in special Geomorphometry More is be discussed landforms and geomorphic processes such as glaciers and runoff in geomorphometry terrain segmentation is an approach for structuring terrain data such as digital elevation model(DEM) via derived land-surface into spatially discrete plan-view areas known as terrain units(strobl,2008).The shape of terrain, i.e. landforms, influence the flow of surface water, transport of sediments, soil production, and determines climate on local and regional scales, furthermore, natural phenomena like vegetation are directly influenced by landform patterns and their relative position across the landscape Landforms although have different meaning to different disciplines, they reveal common physiological and morphological characteristics of terrain which may guide through understanding past and present processes acting on terrain and provide necessary information to related disciplines about land characteristics and potentials(Gerçek, 2010:9). In this study, using MRS algorithms and Ecognition software have been studied glacial landforms the northern slopes of Sabalan.
Methodology In this study, the DEM and derivative of its, have been used in order to extract glacial landforms. To prepare the digital elevation model layer have been used of topographic maps with 1: 25000 scale. Profile curvature, plan curvature and mean curvature are three important layers used in this study.The semi-automated methods refer to the automatic procedures of extracting landform based-process. This is mainly relying on unsupervised isodata classification, pixel-based clasification (supervised /subpixel classifier based on training material), analysis of digital elevation models (DEM), algorithms, hydrological modelling and object oriented analysis (Nabil and Moawad, 2014:42). In this study object-oriented methods and Ecognition software is used for classification and extraction of landforms.
Results and discussion In this study, we are looking for are extracted cirques the northern slopes of Sabalan. This area because of the high altitude receives high humidity this precipitation is mainly in the form of snow and is appropriate for the formation of glacier and for cirques. A cirque is one of the most prominent forms of glacial erosion (Embleton and Hamann, 1988). According to Evans (2004: 154) cirques are “hollows formed at glacial sources in mountains and partly enclosed by steep, arcuate slopes (headwalls)”. In general cirques exhibit concave shape in both plan and profile direction, with a size ranging from hundreds of metres to a few kilometers (Glasser and Bennett, 2004). They are typically composed of a cirque crest, a steep headwall, and a gentler cirque floor often filled by a cirque lake. For extraction of circus having profile curvature, plan curvature and mean curvature layers is necessary. With respect to this aforementioned layers were obtained in the SAGA software.
Scale parameter is a crucial threshold that determines the maximum allowed heterogeneity for segmentation, which has a direct influence on the size of the objects to be obtained. Scale parameter is recognized after a trial and error process (Gerçek, 2010:115). In this study ultimately scale of 36 was selected for the segmentation.
Extracting ridge line position is very important in determining and identifying the the cirque. According to the morphometry of this landforms for classification the ridge were used convex shapes in layers.
In general, the convex shape of the ridge is determined by the positive values in mean and plan curvature. The Ecognition software and MRS algorithm was used for the execution of the model and Segmentation of the layers. Ridge layer was obtained by defining morphometriy characteristics of the ridge and comparing the results with field data. Finally, according to the semantic model of research, the relevant rules defined in Ecognition software and was extracted circus position.
Conclusion In this study glacial landforms (circus) were extracted with semi-automatic approach and by using object-oriented approach in northern slopes of Sabalan. The Result of research were presented in the form of a map which shows the location circuses.

A kind of industrial tourism is profitable and appropriate from economical view that has a special position and role in the development of gepgraphical areas and by creatin of the cultural and social interactions and exchanges among different nations, due to lack of environmental pollutions is very important. Today, numerous theorical and functional experiences in the world for the study, review , planning and management of tourism areas which have an upward trend in line with the technology advances. Natural potentials of each country has a great role in tourism attraction. In this study, the geomorphologic landforms of tourism sample of Zanjan province have been identified and their geomorphotouristic capabilities have been evaluated. Regional geology maps, topographic maps and field data were used for evaluating the landforms of the area and the amount of Landform's geomororphotouristic capabilities have been evaluated based on prolong method. According to landforms, Katalekhor cave landform with 0.73 point of average tourism grade and obtainment of 0.63 of the average productivity value and due to having limestone ornamentations such as stalactites and stalagmite and special shapes as a result of sedimentation or water erosion inside it, has dedicated the most amount of tourist attraction potential and Kharmanhe Sar cave is placed at the second place. Also the erosion phenomenon of Jen Doodkesh in Mahneshan area, by obtaining 0.30 of the average tourist grade and 0.48 of mean of productivity value has dedicated itself the third rank.With respect to the findings of the study for tourism development and sustainable development of this large industry in the considered study area, through informing people for protection of geo tourism attractions which causes the pollution, erosion and damages of the area to be minimized and also through using the investment of private and the state parties in the establishment of infrastructures, it is possible to take steps for development of geotourism in the area, and also in the light of proper and correct management and planning, such geomorphologic phenomenon can be indicated as one of the main important centers of tourism of Zanjan province and even in Iran and in the world.

Landslide is one of the morphodynamic processes including significant hazards in terms of fatalities, financial casualties and the number of happening. In this research, Zoning of potential landslide occurrence is studied in Heyran Defile region. To do this the combination of multi-criteria (Analytical Network Process) and statistically (Baye's theorem) models and accompanied by 12 natural and human parameters including, Slope, aspect, land use, lithology, precipitation, vegetation density index (NDVI), slope length (LS), topographic wetness index (TWI), stream power index (SPI), distance to road, distance to fault and distance to river were used. The layer of occurred landslides in the study area have been used to the obtaining weight of each landslide susceptibility parameters classes and validation of the final map which seventy percent of the landslide for running the model and another 30 percent is used to the model validation. The result is a map classified in five categories that respectively to be included Zones with very low, Low, Moderate, High and very High potential. According to the result map 26.3 percent of the area case study has been predicted as a region with high and very high potential for the landslide occurrence. These areas primarily to be included marginal areas of the Ardebil- Astara road. Most landslides also occurred in these areas because of the high construction in bordering the road, disrupt the natural slope of the land for the road construction and broaden it. Land use in these areas is mainly sparse forest, rangelands and agriculture which is located on the slopes. Evaluation of zoning map was done using 30 percent of the occurred landslide. According to the results of this evaluation and placement of a considerable percentage of landslides in the high and very high sensitivity classes (77.6 %), it can be concluded that the accuracy of used models in the landslides susceptibility zoning is acceptable.

Today, environmental hazards and deal with them is one of the most important concerns for researchers in the field of environmental planning and management of the crisis. In crisis management, flood risk is one of the riskiest natural disasters that have to special look to it. One of the most important tasks in the field of flood mitigation and prevention of occurrence is zoning of potential flooding and classification of the flood risk. In this study Lighvan River Basin has been studied in terms of potential flooding by using ANP and fuzzy logic models and using of natural and human parameters (Land slope, aspect, plan and profile curvature, land use, precipitation, material of land, soil, vegetation density and drainage density). According to results of the research the southern part of Basin (classes with great and high potential) have been introduced as areas prone to flooding that are included respectively 11 and 23.6 percent of the area. More areas with low and very low potential are in areas that are characteristic of often with agriculture and gardens, formations with high permeability and slow slope and low altitude and are included respectively 22.7 and 12.3 percent of the area. These areas mostly are concentrated in the lowlands and slow slope and lower part of the basin where the due to the slow slope and deep sediments and permeable materials have high the posibility of influence precipitation and runoff in this area is very low respective to the other sectors.

Today, natural and rural areas on the periphery of cities are used as a space for urban development. Unthought of land where the quality of ground and loss of ecosystems is critical. Natural features of the surrounding residential areas, especially in the urban and physical planning is very important Because of a phenomenon resulting decision is the best choice. This research led to the development of physical development and Sarein using two models of fuzzy logic and analytic hierarchy process (AHP) was evaluated. The fuzzy logic model for three different values of the gamma function (0.7, 0.8, 0.9) was used for the preparation of zoning maps and compare them with the existing design and development of existing residential areas, favorable and unfavorable, it was found that gamma 0.8 has the highest compliance & About 34 percent of the city In the land and 33 percent of those with a high proportion of In the land was the average suitability & Only 7% of the physical development of land which includes a small proportion of the North, North East, and North West region is The construction beside the river and the fault takes in steep areas. The results obtained from AHP model, particularly in the northern parts of North West and North East fuzzy model is very different And these lands as lands with moderate to high fitness has been introduced. The study was conducted in two models, the results of which were the model of fuzzy AHP model gives more accurate results toward.

Tectonics are dynamic factors that have longitudinal profiles of rivers, especially watersheds are affected.. These factors led to a change in the longitudinal profile of rivers and the erosion and deposition process, thereby changing the pattern of investment flows into rivers. The purpose of this study is to investigate active tectonic basin Gechi Darasi data by geological, geomorphological and observation field, hypsometric curve by using indicators, hypsometric integral, drainage basin asymmetry, the longitudinal gradient of the river, the sine of the mountain front, the width Reverse topographic drainage basin above the valley floor and valley symmetry in this field. According to the results of this study indicate Hypsvmtry integral curves Hypsvmtry young field of erosion cycle and 15 sections and transects to assess VF index specified. Index changes VF (which is equal to 1.2) reflect tectonic activity is active. In addition, SL index is calculated for the basin. Establish fault tolerance on the rock of considerable height difference Gechi Darasi River longitudinal profiles and SL index is a significant change. The basin has a lot of sinus (Smf) in the region of 1.3 is equal to the ratio of the width to the height of the bed of the valley floor is a very large difference. Drainage basin asymmetry index (AF) is equal to 57.45. The topographic indexes with the reversed polarity index (value 0.21) indicate tectonic activities in the region are high.

Introduction Landslide susceptibility (LS) is the likelihood of a landslide occurring in an area on the basis of local terrain conditions (Brabb, 1984). In recent decades number of different types of hazards is greatly increased due to human activities and its encroachment on the natural environment. Compared with other natural hazards such as volcanic eruptions and floods, landslides cause considerable damage to human beings and massive economic losses (Guzzetti, 2005). According to preliminary estimates, about 500 billion riyals annual are caused economic damage in Iran by landslide occurrence (Hosseinzadeh et al., 1388:27). Today Because of the rapid development of computing power and Geographical Information System (GIS) technology, a vast number of quantitative or statistical Methods have been applied to assess landslide susceptibility (Wang, 2013:81). Among these models can be pointed to such as logistic regression model, Analytical Hierarchy Process (AHP), Analytic Network process (ANP), artificial neural network (ANN), the bivariate statistical models, fuzzy logic model. LNRF model and etc. Selecting the most appropriate approach and model is done based on the data type, scale and scale of of the analysis. The work done at the country and abroad can be pointed following cases: Gharahi et al (1390) landslide susceptibility Alborz Dam zonation by using bivariate statistical and AHP model, Results showed that the compared to the weighting factor method statistical indicators zonation provides more realistic distribution of landslide susceptibility. Bidar (1391) has attempted to zonation mass movements in road Meshkinshahr - Movil the advantage of Analytic Hierarchy Process (AHP). He combined with 9 parameters catchment to zonation into four sections with a high risk, high, medium and low risk. Foreign researchers such as Yaklyn (2008) using the Analytic Hierarchy Process (AHP) based on GIS to landslide hazard zonation is investigated according to the characteristics geological formation in the Turkey, And has concluded that in the study area (Ardesen) 98% of the landslides occurred in units with geologic formations susceptible to weathering, steep and bare land. Sabuya et al (2006) the fuzzy logic model used for the assessment of slope instability in Rio de Janeiro of Brazil And found that, in this model the expert can be weighting from zero to one Classes of factors, so the results are better than other models. In this study balekhloo catchment (Ardabil) has Zonation for landslide susceptibility By using fuzzy logic and bivariate statistical Methods And landslide predisposing factors such as (Lithology, distance from fault, distance from river, drainage density, land slope, aspect and land use, distance from roads, vegetation density (NDVI) and elevation). 2- Methodology Fuzzy logic model fuzzy logic model is Generalization of the classical set theory in mathematical science and is a new approach to the expression of uncertainty and confusion routine. Fuzzy sets are defined by membership functions. For each fuzzy set is a number between zero and one Where zero indicates the lack full membership and one indicates full membership (Hosseini et al, 1390). Fuzzy model is done using several functions. The most important operators of the fuzzy logic can be pointed to Fuzzy Product, Fuzzy Sum gamma operator and etc. Gamma operator is defined based on Fuzzy Product and Fuzzy Sum (Equation 1). Equation (1 μ_(combination=((Fuzzy Algebraic Sum)(Fuzzy Algebraic Product))^(1-γ)) Where μ_combination layers obtained from the fuzzy gamma and the γ parameter is defined in the zero and one. Considering studies conducted and their results and compare different values of gamma, in this study, the Gamma 8/0 was used and landslide susceptibility map was produced. Bivariate statistical models Bivariate statistical models have based on overlapping parameters and density of landslides occurred. In these models the relative importance of Classes of each parameter calculated using the Landslide density in its and using the formula. In this method Landslide and parameters are handled as a dependent variable and independent variables respectively. There are several statistical methods to calculate the weighted values that here used the information value and Area density models. 1- Information value method Overall, the combination of quantitative variables (such as slope) and qualitative variables (such as land use) does cause problems in statistical analysis. This problem can be resolved in considering that each class variable individually can to have one of two modes present (1) or absent (0). This method can determine whether the class variable is present or absent (niyazei et al 1389: 12). Mentioned methods runs using the equation 2 (Naderi and Karimi, 1390: 98). Equation (2) I_i=LN((S_i/N_i)/(S/N)) 〖 S〗_i Landslide area occurred in class variable, 〖 N〗_i class variables area, 〖 S〗_. The total area of landslide, 〖 N〗_. The total area of the study area and LN is Logarithm. 2- Area density method In the methods of Area density weight of each class variables is calculated by using the equation (3) (Afjeh Nasrabadi and et al, 1387). Equation (3) Wa=1000(A/B)-1000(C/D) In above equation A is the area landslide happened in each class variables, B area of each class, C the total area of the landslide And D is the total area of the study area. With obtained weight of each class variables, weights of classes applied in software and landslide susceptibility maps were produced for both statistical methods. 3– Discussion According to the method mentioned above layers intended after preparation Were used for zonation And landslide susceptibility zonation map was obtained using mentioned three methods. Maps obtained using the method of natural breaks was classified In 5 risk class (very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility and high susceptibility). For the compare models, were used from Landslide Index (Equation 4) and using it were evaluated entire risk Category. Equation (2) Li=((Si/Ai))/((∑n(Si/Ai)))*100 In above equation Si is area landslide occurred In each zone risk, Ai area of each risk zone, N number of risk Category and Li index landslide occurrence in each risk zone is to Percentage. 4– Conclusion Models used in the study Acceptable results are provided considering was to evaluate using landslide index. Information value models with values of 10 and 87percent in the high and very high risk Classes Have a better Evaluation than two other models. Area density model with values of 9% and 87% and fuzzy logic models with values of 24% and 60% in the high and very high risk Classes Are placed respectively the next ranks. Evaluation results indicate that in Information value models about 98 percent of landslide occurred in high and very high risk classes. This value has been estimated 97 percent for Area density model and 84 percent for fuzzy logic model. The results from each of the three models indicate a high potential for landslides occurred in western and southwestern parts of the basin. This areas are formed more of sedimentary rocks, old terraces and various combinations of clay, marl, limestone and Lahar. This is indicative of great influence of lithology and Type of formation to create landslide.

Landslides are amongst the most damaging geologic hazards in the world. They pose a threat to the safety of humankind lives as Well as the environment, resources and property. Compared with other natural hazards such as volcanic eruptions and floods, landslides cause considerable damage to human beings and massive economic losses (Guzzetti, 2005). According to preliminary estimates, about 500 billion riyals annual are caused economic damage in Iran by landslide occurrence (Hosseinzadeh et al., 1388:27). Much literature available on landslide hazard assessment methodologies broadly falls into three main Approach groups: qualitative, quantitative and artificial intelligence (AI) approaches. in general, a qualitative approach is based on the subjective judgment of an Expert or a group of experts whereas the quantitative approach is based on mathematically rigorous objective Methodologies. Artificial Intelligence (AI) techniques can make use of heuristic knowledge or pattern matching technique as opposed to solving a set of mathematical equations. The AI broadly covers Artificial Neural Networks (ANN), Expert system, and other heuristic knowledge-based or rules-based techniques. (Neaupane and Piantanakulchai 2006:281). For the Landslide Susceptibility Mapping can be used a variety of models, such as logistic regression, Analytical Hierarchy Process (AHP), Analytic Network process (ANP), artificial neural network, the bivariate statistical models, LNRF, fuzzy logic models and etc. Usually choose the most appropriate approach and model is done based on the data type, the scale of the study area, the scale of analysis and Knowledge of researcher. Study area: In this study siyahrood catchment has Zonation for landslide susceptibility by using weights of evidence models (Baye's theorem). The basin is located in the province of Gilan. The catchment area is 437 km and is sub-basins of the Sefidrood River. The weight of evidence (WofE) method (Bonham-Carteretal.,1989) has been used to evaluate shallow-landslide susceptibility and has been tested as a useful spatial data model for various applications including mass-movement studies, mineral research, and groundwater spring mapping (Mark and Ellen, 995; Poli and Sterlacchini,2007; Barbieri and Cambuli, 2009).. It takes into account the relationships existing amongst the occurrence of a supporting evidence (shallow landslides in this study) and the distribution of causal factors (shallow-landslide predisposing factors in this study) The WofE is a statistical method based on the Baye's theorem (Denison et al., 2002). This methods first applied to mineral exploration in1988 (Bonham-Carter et al., 1988) and Subsequently, Van Western (2002) applied the method for Landslide susceptibility assessment. Bayes’ theorem can be written as: Equation (1) P(s│B_i)= (P(B_i│s)×P(s))/(P(B_i)) Where P (Bi | s) is the conditional probability to have Bi given s, P (s) is the prior probability to find s within the study area (AS) and P (Bi) is the prior probability to find the class Bi within the study area AS. In this study landslide susceptibility zonation has been done using several natural and anthropogenic parameters Such as (lithology, distance from fault, distance from river, rainfall, land slope, aspect, land use, vegetation density (NDVI) and sediment transport index (STI) stream power index (SPI) and topographic wetness index (TWI)). After the Weights classes were obtained using the model for each parameter, Weights was applied to each class in the Arc map software and eventually with overlay parameters was obtained landslide susceptibility maps. The final Maps was classified In 5 susceptibility class using the method of natural breaks (very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility and high susceptibility). According to the results of the model and the map developed in the lithology layer, Most of the weight is allocated to Class B (old alluvial terraces and High alluvial fans). Moderate range among the different classes of land use and north and northwest directions in the aspect parameter Have the greatest impact on landslide occurrence. As well as slope of 20-10 degrees and 10-5 degrees, respectively and in the layer distance from the river, 100 meters from the river have the greatest impact in landslide occurrence. Assessment models with using landslides occurred in the area show that with increasing risk class, landslide density in the class increases And 59 % of landslide, has occurred in very high susceptibility class. While the area of this class compared to total area of the region is only 10.5 percent. Although Classes with very low susceptibility, low and moderate susceptibility are included approximately 71 percent Area of a Region, But only a small portion of the landslides occurred (16.7%) in these classes While the roughly 83. 3 percent of landslides occurred in the area are located in the fourth and fifth class (high and very high susceptibility). Due to this can be said that the model has a good functionality in the area terms of the prediction of landslides.

Because of providing proper living conditions, high soil fertility and accessibility to water resources fluvial environments have always been considered as desirable areas for human beings. Such areas, especially the stream environments have also been dynamic areas in terms of erosion processes. A study of bank erosions, sedimentation and changes occurring in the current river beds, streams patterns and the like can be an important contribution to the field of nature management. This study has tried to use satellite imagery (Landsat), topographic and geological data, to investigate and assess the morphological changes which have occurred at two parts of the Zarine River (Jigatoo River). ArcGIS and ENVI software were the most important tools which were used to prepare and extract data in this study. According to the results, the second part of the area under study, which is located in a plain area, has been severely affected by the faults, and the direction of the river flow was forced to change path at several points following a fault path. The river pattern at this area is direct and in some cases the sinusoidal in sectors has been affected by the fault, while the general pattern is meander in other parts of the river. In the three time periods considered for the study both intervals number 1 and 2 of the riverbed area show reduction. This is to say that the area of the riverbed has reached 5.6 sq. km to 5.43 sq. km in the part of 1 and 6/19 km to 4.87 in the part 2. The river bank erosion rate between 1975 -1989 and 1989 - 2010 is respectively approximately 4.2 sq. km and 2.44 sq. km in part 1. This value for the second interval is respectively 2 and 1.49 sq. km. As a result of this destruction the large parts of the riverbed has dried due to redirection and relocation or has turned into meander lakes on the margins being respectively 3.44 and 2.48 sq. km in part 1and 3.29 and 1.52 is km in part 2 for the years 1975 to 1989 and 1989 to 2010.

Catchment land use changing in an area is one of the most important factors in hydrology. As a model, L-THIA was designed to assess the long-term impacts on the hydrology of a catchment for researchers who wanted to determine the relative changes in the runoffs from one land-use condition to another. In this regard, upstream section of Gharasoo catchment in Ardabil province has been evaluated in this study in terms of land use change (1987-2012) and the impacts on runoff production. To accomplish this end the research has used daily precipitation data from four stations, Landsat Satellite images (TM and ETM sensor), L-THIA extension software and Arc Map software. Modeling results indicate that during the 25 years, the land use change has caused an average of 1.8 mm increase in the runoff in this catchment. Land use changes mainly increase the expansion of residential areas, and loss of woodland and pastures. In some areas, such as Ardabil plain, due to conversion of pastures into farmland, land use changes have operated in a positive direction increasing the permeability of the ground and have mutually reduced runoff in this part of the catchment. Due to its capabilities in providing a zonation map, the volume and depth of the runoff, the model used in this study has the required ability to show the hydrologic impacts of land use change and demonstrate susceptible areas and flooding in the basin.

Restrictions and seasonal attractions of the area can lead to confusion and perhaps cut the tourists from the region, the season of the visit is inappropriate. Therefore, it is necessary, suitable tourism zone is specified for each season. In this study to determine the suitable zone for tourism in the four seasons, the two models have been used bioclimatic (TCI) and Analytical Hierarchy Process (AHP). bioclimatic model (TCI), 7 of climatic parameters for climatic conditions, is used.: Average monthly rainfall, average temperature, average relative humidity, Maximum temperature, minimum relative humidity, mean daily sunshine hours and wind speed While the hierarchical analysis, we tried to consider the impact of climate indicators Attractions in every season. Finally, by comparing model output with reality in the region, was determined Analysis of the hierarchical model, suggest that the actual zones more than bioclimatic model, for tourists.

In this research the runoff coefficient and effective elements that affect the floods risk of Balekhlo basin were estimated and studied. In this area (Balekhlo River basin) to estimate the amount of runoff the curve number method (CN) were used. This method is the most common method to predict runoff volume. For this purpose, the data and information required includes: the maximum daily rainfall statistics, maps Density of vegetation, soil and Hydrologic Soil Groups were prepared and entered into Geographic Information System (GIS) environment. Combining these data with the model (SCS), maps (CN) basin, the amount of precipitation (S) and runoff (Q) was prepared. Finally, the hierarchical analytical process was used and six factors include: runoff, hydrological soil groups, slope, vegetation density, land use and drainage density were weighted using AHP method. Finally, the zonation map of flood potential produced with different return periods (5, 15, 25 and 50 years) by weighed layers. Results showed that about 40 to 47 percent of watershed areas, during periods of 5 to 50 years have moderate flooding potential.

Natural parameters are the main and defining factors in physical development aspects of the cities and habitats. The effects of these factors as developmental obstacles are doubled in a mountainous region and can cause natural hazards. In this study we tried to identify the optimal directions of physical development in Divandareh as a mountainous region through recognizing and evaluation of influential factors. To do so, 10 effective indices are used and for evaluation, modeling and predicting the suitable areas of physical development of the city, the fuzzy logic model is used so that each layer are entered into the ARC GIS 9.3 software. And then product, sum and various amounts of Fuzzy gamma operator are implemented in these layers.. Analytical comparison on suitable zones of the present situation of city based on critical points with appropriate zones obtained from gamma amounts were done in order to select the optimal fuzzy gamma and it was identified that 0.8 fuzzy gamma has the maximum compatibility with the suitable lands for existing condition of the city. Then final map was grouped into 5 classes and it was determined that 24 percent of the study area (28.87 km2) is located in the class with the high suitability. 56.7 percent of the area (68 km2) is located in a unfit suitability class. Based on the final classified map of we can conclude that the suitable zones for future physical development of Divandareh are mostly located in east, partly in northern and south-eastern areas in a disperse manner.