The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher r

The study of groundwater resources in relation to topography is important. Clearly, in different topography, depth of the water level is different. Therefore, the aim of this study is the determination of the relationship between landform classes with compound topographic index (CTI) and depth of the water for the Maharlou-Bakhtegan watershed, Fars Province, Iran. In order to evaluate the depth of the water for the study area, CTI and geomorphology (landforms) were derived from a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The results of landform classes extracted using topographic position index (TPI) showed that the largest landform is open slope, while the smallest are plains. It was found that CTI and depth of

A method is presented allowing farmers at Shiraz in the Fars province of Iran to balance in between their budget and the soil parameters. First, the three alternatives (Best-First, Greedy-Stepwise and Ranker) of the Feature Selection Method identify the most critical soil fertility parameters. Training data model evaluation indicate that the Greedy-Stepwise feature selection algorithm (with attribute evaluator of CFS-Subset-Eval) presents the highest accuracy for the particular study area. Soil fertility is found to highly depends on Potassium, Phosphor, and Organic Carbon while Copper, Iron, Manganese, and Zinc dependencies are rejected. Finally, by utilizing Ordered Weighted Averaging, six maps with different risk levels in terms of the s

The aim of this study is to perform landform classification in the south of Isfahan, Iran. In order to make landform classification, Digital Elevation Models (DEMs) with 30 m resolution and Topographic Position Index (TPI) were used. Also, the percentage of each landforms classes in geomorphological units was determined. The results show that there were ten classes for the study area that consist of classes of canyons, shallow valleys, upland drainages, U-shaped valleys, plains small, open slopes, upper slopes, hills in valleys, midslope ridges and mountain tops. Also, the results show that the highest area of landform was for the classes of plain small for Playa unit. While for pediment unit, the area of landform was canyons, streams, U-sh

The occurrence of earthquake has made human being consider fundamental plans to reduce the consequent danger and destruction. The only means to reduce the vulnerability is to set specific management of urban crisis in construction; moreover, this aim cannot be achieved unless the city immunity, in confrontation of the earthquake, is considered as a major purpose in all stages of urban planning. Proper allocation of various urban land uses helps hugely the process of management of urban crisis related to the earthquake; accordingly, recognizing the different effective variables of vulnerability of urban areas from the aspects of urban land use, definition and declaring their relations with vulnerability, their analysis and finally preparing

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and co

Predicting land suitability is an important prerequisite for estimating and mapping unknown land suitability values. Fuzzy spatial prediction techniques are suitable to determine areas where our observations are imprecise and vague. One of the techniques for this purpose is fuzzy membership kriging with a semi-statistical membership function. The implementation of fuzzy membership kriging extracts semi-statistical membership functions from data, and applies these functions to an indicator kriging model. In this study, we use fuzzy membership kriging and indicator kriging to predict land suitability based on nine characteristics including topography, wetness, salinity (EC), alkalinity (ESP), soil texture, soil depth, CaCO3, pH (H2O) and Gyps