جستجوی مقالات مرتبط با کلیدواژه « digital elevation model » در نشریات گروه « جغرافیا »

In this study, 5 main DEM derivatives of 12.5 m ALOS satellite (slope layer, slope direction layer, curvature layer, cumulative flow layer and altitude layer) as well as Sentinel-2 satellite images and NDVI vegetation index were used as auxiliary layers. Segmentation in this area was performed using segmentation multi resolation method. In this segmentation, the height layer was given a value of 3, the curvature layer was given a value of 2, and the other layers were given a value of 1. Then, using Layer Values ​​and Geometry algorithms and assign class commands, landforms located in the western and southwestern slopes of Zagros (Aligudarz city area) have been classified. The results showed that the use of Layer Values ​​and Geometry algorithms and assign class commands have a good ability to isolate and classify landforms, so that 8 types of landforms (slopes, ridges, water areas, precipices, peaks, ridges, lowlands and lowlands) Kappa coefficient was 0.87 and overall accuracy was 91.71%. The ridge landforms form the largest part of the region and are the dominant landforms of the region and have a good distribution in different parts, but the peak landforms with the minimum area have formed only a limited part of the study area.

Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).

The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.

In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.

The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.

Digital elevation models and its derivatives are important factors for watershed modeling. It is obvious that DEM errors adversely affect the accuracy and thereby modeling of natural processes. This problem along with the impossibility of measuring all elements of nature, has led to a major evolution in the way of understanding and explaining phenomena. In this way, we can use the fractal geometry as a quantitative tool for modeling many complex natural phenomena. In fact, geophysical phenomena such as drainage networks are fractal phenomena with fractal behavior. The purpose of this paper is to evaluate sensitivity of the drainage networks based on DEMs (ASTER & SRTM), flow direction algorithms (Single Flow Direction (D8) and Multiple Flow Direction (MD8)) and topographic maps of 1:25000 in order to study the fractal dimension of drainage network on geological formations of Yazd-Ardakan basin. The results showed that the least difference in the length and the rank of the stream belonged to the drainage network obtained from the topographic maps of 1:25000. After the topographic maps, ASTER and the multi-flow direction algorithm are close to real ground map. Even though the multi-flow direction algorithm shows more detail on the drainage network. But it is not close to real ground map. The difference is particularly noticeable in the first rank of streams. Due to the very high sensitivity of the fractal dimension to the smallest change in drainage network conditions, the drainage network obtained from topographic maps were used to calculate the fractal dimension. The mean fractal dimension of 1.149, 1.16 and 1.207, respectively, represents Taft, Granite and Kahar formations. There is a significant correlation between fractal dimension and sensitivity to erosion of geological formations (level 0.99). In fact, the fractal dimension increases with increasing the sensitivity to erosion along with the drainage density in geological formations. The results showed that fractal dimension allows for a quick and accurate analysis of sensitivity to erosion of the formations of this area.

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.

Identifying flood-prone areas is one of the essential strategies in planning to mitigate the damaging effects of floods. In this study, topographic and morphological indices were used to investigate flooding. Due to the effect of hydrogeomorphic features on flooding, these features were extracted by ARCGIS software and watershed modeling system with the help of digital elevation model and topography layers. Due to lack of accurate field data and incidence of soil moisture, vegetation layer and precipitation statistics from remote sensing facilities, soil moisture and precipitation were extracted. In order to control and compare the information extracted from precipitation satellite images dated December 03, 2016, it was selected as a flood sample. Due to the importance of topographic moisture index to describe soil moisture conditions and estimation of physical and hydrological characteristics, this index was used and the output map was classified according to the area. To illustrate flood-prone areas, a hybrid model was used in which the soil surface moisture layers (optical trapezoidal model was extracted from Landsat 8 images), vegetation, precipitation and topographic moisture index were applied. After mapping the maps and weighting, the layers were merged into GIS environment and the flood potential map of the basin was extracted and the basin was flooded with five flood susceptibility ranges, moderate floods, partly floods and floods, respectively. The flood was classified. According to the extraction map and analysis, out of 3279 km2, about 81.6 km2 (2.5%) were susceptible to flood and 1.9% of the area with moderate risk of flood was identified. Most flood prone areas are located in the central plain and the north of the basin in the flat marginal lands of the Simineh River.

Flooding is a natural hazard in many parts of Iran and has been increasing in intensity and frequency in recent years. Studies show that flood damage is not caused by increased frequency, or magnitude of floods, but by the extensive use of floodplain lands. Therefore, it is necessary to formulate a comprehensive plan with the aim of controlling, inhibition and optimizing the use of management measures appropriate to all the factors involved in the creation of regional floods.For flood control, flood zoning is one of the best methods for planning and identifying flood sensitive areas with the aim of reducing flood hazards, and identifying flood zones using mathematical models that combine topographic and morphological indicators can be used in the program. Plans can be very effective in reducing risks and injuries.Laval and Omodoji (2017) studied, correlated and significantly correlated vertical roughness measurement surface roughness index (VRM), topographic roughness index (TRI) and topographic wetness index (TWI) in the detection of flood and non-flood areas. The effect of topographic wetness index was higher than other indices.Given the limitations and scarcity of field record information in developing countries, the use of satellite imagery, including TRMM satellite data, can fill the gap.

The study area of the Simineh river basin is about 2977 km2 from the sub basins of the Urmia Lake Basin. Hydro geomorphic features of the basin have a significant effect on the flood potential and are affected by two factors causing flood. One is the topographic and altitude features that increases rainfall and the flow of water. The other is the use of geomorphic features of the basin to estimate the flood potential that has been used in many parts of the world In this study, a digital elevation model (DEM) with a resolution of 30 m extract of ASTER image was used. The physical properties of the basin were extracted using Archydro application in the ARCGIS10.2 software environment. In the next step, the necessary analyses for the preparation of the topographic humidity index and other parameters were extracted according to Horton parameters.Among topographic characteristics, the topographic Wetness index is a useful and common tool to describe the humidity conditions in the scale of the basin. In the extraction map, the highest value is for the areas with a higher TWI and lower values related to the areas with a lower TWI index.

The results of TRMM satellite images were used to control and verify the recorded statistics of ground stations. For this incident, 02 December 2016 precipitation were selected as a partial incident. Several factors such as the occurrence of heavy rainfall, the sudden melting of snow in the mountainous region, or the simultaneous operation of both factors have been noted in flood formation in a basin.In this study, the precipitation layer of the meteorological stations is used and compared with the information obtained from TRMM satellite images. Soil moisture index from Landsat 8 satellite images was extracted and used by OPTRAM model on December 19, 2016 and NDVI index was extracted using Landsat 8 images on the same date.After extracting the required maps of the model, all maps were matched in order to match the nearest neighboring method. After implementation of the model, a map of flood risk distribution was prepared. According to the extracted map, and based on the experience and the results of other studies, the total area of ​​3279 square kilometers is about 81.6 km2 (2.5%) prone to floods and 62 km2 (1.9%) with a moderate risk of flood. The area is more prone to floods in the plains of the central and northern basin.Descriptive and spatial information on flood prone areas was collected to identify and obtain the information from high risk areas. These areas are mostly located in the plain and north of the basin on the flat marginal lands of the Simineh River, with 18 villages in the area prone to flooding

The study showed that high resolution satellite images such as TRMM are suitable to estimate and severe precipitation, as Islam and colleagues (2010) concluded in their study. The topographic wetness index (TWI) is derived from a digital elevation model (DEM) and hydrological features can be extracted using ARCGIS and WMS software. As Banon et al. (1979) extracted the hydrological characteristics using the topographic index.To complete the information layers used in the model to extract the flood-prone area, precipitation data were extracted from TRMM satellite images and soil moisture using OPTRAM model and vegetation from Landsat8 satellite images. After combining the layers, three layers of classification including topographic moisture, precipitation and soil moisture mapping were assembled using raster analysis with different weights. Finally, spatial hazard distribution map of floods was extracted, with flood intensity of five flood prone areas, respectively, partly flooded, with very low floods and no floods.

Excessive use of fossil fuels threatens the living environment on the planet by contaminating the environment, and today one of the solutions proposed for energy is the use of renewable energy along with the reform of consumption. solar radiation zoning map of the city of Sabzevar was produced 159 active village. and then the annual radiation pattern received in 2017 by radiation analysis and using DEM of the area with power The spatial resolution of 30 m was estimated for point-to-point radiation and finally, based on the village's demand and solar power production potential in the potential region. In order to better evaluate, the study area was studied in three parts of central Sabzevar, Roodab and Shastshmat. The central part of Sabzevar with the value of 90201150 and the sixth part with 66910770 watts per m2 have the highest and lowest total radiation, respectively. In order to determine the most important factor affecting irradiation, the correlation between the two factors of height and sunshine with total radiation was calculated and the results indicate that the correlation between total radiation and the height factor is far more than sunshine hours. Finally, the total radiation potential at the study area was calculated and evaluated, the results show that the potential of solar energy is scattered in rural areas with a low population density. The results also show that 95.82 percent of the city's surface has a high potential, 4.01 percent has a very good potential and 0.15 percent has a good potential.

The climatic conditions of each site play an important role in the dispersion of humans, animals and plants. Therefore, any activity or planning in different economic, agricultural and industrial fields at the ground level is not feasible without the knowledge of the climate. For this reason, climatic zoning and recognition of the most important factors and factors affecting each area is one of the ways of recognizing the climatic identity of the area. Lack of knowledge of the sub-regions of the country fails to meet the economic and agricultural plans of mankind. In general, the climate of a region is the average of the weather conditions in the region. Access to the average weather conditions in a specific location requires long-term weather information.
Data and In order to obtain the correct and comprehensive knowledge of the climate of Hamedan province, climatic zoning was performed with new statistical methods such as factor analysis and cluster analysis during the 20 years period (1993-2013). For this purpose, 23 variables were selected from 8 meteorological stations. Then, using a digital elevation model, a multivariable regression was applied between the meteorological parameters and the digital elevation model. Finally, a zonal matrix with a dimension of 23 × 88 was obtained. Since the aim of this research was the climate zone of Hamadan province based on altitude, a digital elevation layer (DEM) was used with a resolution of 90 meters. In the following, for climatic zoning, a regression relationship was made between climate parameters and length, width and height of the area. To identify the climatic sub-regions of Hamedan province, the raster data obtained from the zoning were converted to point data. Then, based on the analysis of the main components, the points were analyzed by clustering method and the dominant factors were identified. In this research, the resolution of each of the pixel was 15 × 15 km and a matrix with dimensions of 23 × 88 was developed. Finally, this matrix was clustered into the MATLAB software using the Wardclustering method.
By studying 23 climatic elements, 5 climatic factors were identified and their maps were drawn. These factors include temperature, visibility, rainfall, thunder storm and radiation. Among these factors, the first factor with 37% of the variance of the total data has the most important role in determining the climate diversity of the province. This factor is most commonly observed in the south and southwest of the province and with moving to the north and northeast of the province, this factor is severely reduced.
According to the dendrogram, 6 climatic regions were identified and the characteristics of each separate area were investigated.

Digital Elevation Models (DEMs) is one of the main geographical data models which form the basis of the different spatial analysis. DEM is known as fundamental data in for many modeling tasks. Nowadays, the results validation of GIS spatial analyzes, has become a major challenge in the world of GIS .The quality of a DEMis dependent upon a number of interrelated factors, including the methods of data acquisition, the nature of the input data, and the methods employed in generating the DEMs.Analysis of uncertainty in different fields, due to data quality and related issues such as error, uncertainty models, errors propagation, errors elimination and uncertainties in the data, are felt more than any other times. Of all these factors, data acquisition is the most critical one. Previous studies on DEM data acquisition have focused either on examination of generation method(s), or on case studies of accuracy testing. These studies are not adequate, however, for the purpose of understanding uncertainty (an indicator used to approximate the discrepancy between geographic data and the geographic reality that these data intend to represent) associated with DEM data and the propagation of this uncertainty through GIS based analyses. The development of strategies for identifying, quantifying, tracking, reducing, visualizing, and reporting uncertainty in DEM data are called for by the GIS community.
In order to apply uncertainty analysis on DEMs this study aimed to evaluate the error rate and uncertainty of elevation data obtained from SRTM and ASTER satellites. The objectives of this study are: (1) to understand the sources and reasons for uncertainty in DEMs produced by cartographic digitizing; (2) to develop methods for quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimize the chances of error by manse of optimizing models. Quantifying uncertainty in DEMs requires comparison of the original elevations (e.g. elevations read from topographic maps) with the elevations in a DEM surface. Such a comparison results in height differences (or residuals) at the tested points to analysis the pattern of deviation between two sets of elevation data, conventional ways are to yield statistical expressions of the accuracy, such as the root mean square error, standard deviation, and mean. In fact, all statistical measures that are effective for describing a frequency distribution, including central tendency and dispersion measures, may be used, as long as various assumptions for specific methods are satisfied. Our research methodology includes several steps. The first step causing the statistical indices ME, STD and RMSE, the error rate of DTMs ​​ for obtaining the chances of error in ach model. It has to be mentioned that the main attraction of the RMSE lies in its easy computation and straightforward concept. However, this index is essentially a single global measure of deviations, thus incapable of accounting for spatial variation of errors over the interpolated surface. In order to obtain more accurate results, then uncertainty of data errors was also simulated by Monte Carlo method and error propagation pattern was extracted by interpolation of results.
The results of this step show that, the DEM derived from pair stereo ASTER despite having better spatial resolution, included more errors and practically lacking the details of DTM 30 meters. Finally, removing the error propagation pattern from DEMs, the secondary DEM was produced. By recalculating indicators describing the error and comparing these values with the initial values, the results indicate that, both DEMs show more accuracy after eliminating the error propagation pattern. TPI Index was used to determine the location of basin topography and the basin is divided into 6 classes and error rate in each class was calculated before and after the simulation. The results showed that, the error rates in all classes before and after the simulation in both DEMs were reduced. In terms of uncertainty analysis methods for DEMs, results of our research indicated that the RMSE methods alone is not sufficient for quantifying DEM uncertainty, because this measure rarely addresses the issue of distributional accuracy. To fully understand and quantify the DEM uncertainty, spatial accuracy measures, such accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used results also indicated that Monet Carlo simulation is indeed sufficient methods for simulation error in DEMs. Results of this research are great of important for uncertainty analysis in domain of Geosciences and can be used for improving the accuracy of modeling in variety of applications.

During recent decades in water resources engineering sciences, the prediction of suspended sediment load particularly in flood areas was highly regarded. Nowadays Methods and artificial intelligence techniques to predict hydrologic have become very popular. In recent studies of various parameters such as the spectral reflection bands of satellite images, land use, geology and climatic data have been used. Landsat satellite images according to their high resolution has good spatial resolution. Da Silvia (2015: 53) spectral calibration multispectral satellite images to assess their suspended sediment concentration. Their results showed that the concentration of suspended sediment has been strongly influenced by seasonal rainfall. The yellow river sediment using landsat satellite images by Zhang et al (2014:136) were evaluated. The results showed that, using the modified algorithm and recovery appropriate climate models, TM / ETM can be used to quantify the concentration of suspended sediment at the mouth of the yellow river. In this study, mining indices satellite images and watersheds geomorphometry parameters that derive from the characteristics of the basin surface to evaluate and compare the performance of these parameters to predict suspended sediment has been studied. In this study, methods such as artificial neural networks, linear regression, K nearest neighbor, Gaussian processes, support vector machine and evolutionary support vector machine selected and with purpose check the role of these parameters were used to predict suspended sediment load,to detection of the impact of these parameters is to improve the assessment models.
Study Areas
There were 68 catchment areas located in the provinces of Gilan and Lorestan from Iran
Data processing
Data mining geomorphometry
After determining the area of study geomorphometry parameters were extracted. Geomorphometry parameters was extracted from 30 meter area digital elevation model
The modeling process
In this study the input parameters in the prediction of suspended sediment load of data mining models such as linear regression, Gaussian processes, neural networks, k-nearest neighbor, support vector machine and evolutionary support vector machine was used.
Linear regression
Linear regression to model the value of a quantitative dependent variable that is based on a linear relationship with one or more independent variables used.
Artificial Neural Network
Artificial neural networks including computational models that can be used even if the relationship between inputs and outputs of a physical system is complex and nonlinear, with a network of interconnected nodes that are all are joined together.
K-Nearest Neighbor
K-Nearest Neighbor algorithm including the selection of a specific number of vector data then randomly from the set for the simulation period following is a given period.
Gaussian process
A Gaussian process is a stochastic process which consists of random values at any point in space or time domain so that each of the random variables are normally distributed.
Support Vector Machine
Support vector machines are a class of supervised learning methods for classification and regression problems applied.
Evolutionary Support Vector Machine
Evolutionary vector machine model use of an evolutionary strategy to optimize its. It offers an evolutionary algorithm to solve the problem of dual optimization a support vector machine.
Evaluation Model
In order to evaluate the algorithms applied to the data, the evaluation criteria Root mean squared error (RMSE), relative error (Re), Correlation coefficient (r), Absolute error (AE) was used.
Weighting parameters
In this study, for the weighting input parameters of support vector machine algorithm used, this algorithm
coefficients a normal vector of linear support machine as the weight of characteristics determines.
At first the different algorithms on the data geomorphometry parameters were applied. The results showed that with using geomorphometry parameters Gaussian process model with RMSE = 10.35 and R = 0.986 is the best model to predict suspended sediment load. In the next phase models, were used on the input data indices satellite images. Then index satellite images and geomorphometry parameters as input been togather and models were run on them. Also results showed the Gaussian process model RMSE= 5.026 and R=0.99 has highest accuracy in predicting suspended sediment load.
Discussion and The use of indices satellite images and geomorphometry parameters as model input cause increases the accuracy of data mining algorithms to predict suspended sediment load. The results of the study indicated that satellite imagery indices has been more effective in predicting suspended sediment load and using these indicators increase the accuracy of models more effective than geomorphometry parameters. Therefore, considering the indices of satellite images, Gaussian Process Model with RMSE =7.513 and also, if using the geomorphometry parameters of the Gaussian process model with RMSE =10.35 has Highest accuracy. By combining geomorphometry parameters and indicators has increased the accuracy of all models and Gaussian process model with RMSE =5.026 had the highest accuracy. The results of weighting also showed influence of indices satellite images to predict suspended sediment load.

The exact estimation of qualitative and quantitative characteristics of the natural phenomena requires a great deal of time and expense. Therefore، the interpolation procedures are used as a suitable solution in the estimation of the places which are uninformed to other researchers، and haven''t been sampled yet. The reason for the choice of the mentioned procedures is that there are many maps derived from the Digital Elevation Model، and these maps have application in different analyses، and also they may have influence on the result of an analytic research، using different scales. If the main source which is Digital Elevation Model malfunctions، the whole project will have more errors. The main subject of this research is the choice of the most suitable procedure for the formation of the Digital Elevation Model which can present more real-life model from the natural condition of the earth، with high precision. The research purpose is the comparison of some of the procedures for the preparing of the Digital Elevation Model، including TIN (Triangulated Irregular Network)، TOPOGRID، and also Digital Elevation Model derived from 3-dimensional satellite images. The mentioned procedures were performed on the data of theNoferest watershed in 40 kilometers of the east of Birjand city and their results have been compared precisely with each other and also with the data derived from the 2-frequency global location finder systems (DGPS). The results obtained from the comparison of the amounts of minimum and maximum differences and also the comparison of the RMSE amounts between the Digital Elevation Model and the DGPS data in 10 research zones، show that TIN procedure which has obtained the maximum amount of difference، 5 times with no minimum amount، has more errors، and the Digital Elevation Model of SRTM which has obtained the minimum amount of difference، 3 times with no maximum amount، has higher precision. In this procedure، the TOPOGRID and TOPOGRID INFORCE and also the Digital Model derived from the ASTER images، are placed in the subsequent ranks.

Planting date plays main role in crop performance. Planting date through correspondence with the climatic elements affect vegetative and reproductive growth and ultimately affect the quality and quantity of crops. Among the climatic elements, temperature and day length are more important under irrigated condition. It is necessary to mention that the majority of crops cultivated in Iran are indifferent to day length. The temperature is the most important element in controlling their growth period. By using long-term weather data and related software such as Arc map we can determine the suitable planting dates for a wide area. Therefore, by eliminating field experiment and avoiding large amount of time and cost, much can be saved. The purpose of this study is to determine the best planting dates for spring safflower in different parts of Esfahan province in order to gain the maximum performance in any climatic zone. The minimum, maximum and mean temperature of 51 synoptic and climatic stations of Esfahan rovince and other neighbouring provinces from 1961 to 2011 have been used to determine the ppropriate planting dates of spring safflower in Esfahan province. Using the mean temperature nd Kriging method, Esfahan province is divided into three zones including zone 1 (warm), one 2 (moderate) and zone 3 (cold). For determining the planting dates of spring safflower in ifferent part of Esfahan province daily mean and minimum temperature from January to ctober as average of 15 days have been calculated and related maps were plotted in GIS nvironment. Interpolation of temperature was done by Digital Elevation Model (DEM) and regression analysis between temperature and height in the GIS environment. Beginning of planting dates in warm, moderate and cold regions were considered to reach mean of temperature to 7, 9 and 12°C, respectively. For determining the growth inhibitory of high temperatures, average of the 15-days mean and maximum temperature calculated from June to September and related maps were plotted in GIS environment. The daily mean temperature of 30°C and the maximum temperature of 37°C are considered as high-temperature inhibition. Delay in spring planting of safflower accelerates the development stages, decrease vegetative growth and yield components, and ultimately cause safflower yield reduction. Early planting dates due to production of higher seed yield are recommended. Thus, if the thermal requirements of safflower provide the safflower cultivation, earlier and higher yield will be achieved.In the first thermal zone, information layers of the regions were combined that in them mean temperature is reached to 7°C and the minimum temperature to above 0°C. Therefore, in mid-January the eastern and northern half of the province is appropriate for safflower cultivation. In this zone, in east and north parts of the province the planting dates start at January 19 and end in March 6. Khorobiabanak and Biazehbiabanak are stations that located in this region. In the second thermal zone, information layers of the regions were combined that in them mean temperature reached to 9°C and the minimum temperature reached above 0°C.Therefore, in mid-March the areas of south-eastern and central provinces were added to the previous range. In this zone, on some parts in south of the province the planting dates start at March 7 and end at April 4. Esfahan, Kabootarabad, Palayeshgahe Esfahan, Najafabad, and Balan stations are located in this region. In the third thermal zone, information layers of the regions were combined that in them mean temperature reached to 12°C and minimum temperature reached above 0°C. Therefore, in mid-April, additional narrow strip of the northwest to the south of the province was added to the previous range. In this zone, in the other parts of the province the planting dates start at April 5 and end at May 21. Golpaygan, Meymeh, Abyaneh, Daran, Singerd, Chadegan, Emam Gheys, Mehrgerd and Hamgin, Damaneh Freydan, Freydoon Shahr, Badijan, Hana and Khonsar stations are located in this zone. It is noteworthy that in the west and north western part of the province some regions with 2338 to 4405 m heightare not suitable for safflower planting due to low temperature. Based on the results in the first, second and third thermal zones, planting dates in the province is generally started from January and continue to May.. By considering temperature requirements of safflower the suitable planting date must be considered. Cultivation and planting shall not face to limited temperature and in every zone the first planting date is the best time for planting.

Since the avalanche hazard map is a very time-consuming task, our goal is to improve the development of risk mapping models in vast remote areas. This model is based on satellite imagery and digital elevation model at two points of the Swiss Alps. To simulate avalanche hazards, the model (DEM) is programmed in a computer that includes the determination of avalanche coordinates and parameters in the forest environment. Forests and pastures were classified according to thematic maps (TM) data. So far only a single forest classification has been made. While separating forests, bushes, and areas near the separating lane (the hypothetical line near which no tree grows) creates problems. The classification of small water springs, and the effect of avalanches within the forest was successful. The comparison of Bahman land and land use maps shows that 85 percent of risk and risk areas are correctly categorized. But for scientific applications, the separation of the red and blue lines was not satisfactory, and more needs to be done for operational applications that need to be addressed. The overall policy is very promising and should be led toward our goals, which is to provide more reliable risk maps with better and newer gateways to mutual conversion of snow and forest.