Alireza Arabameri; Mohammad hossein Ramshet; Khalil Rezaei; Masoud Sohrabi
Abstract
Nowadays, desertification as a great problem affects most of the countries in the world, especially developing countries. Desertification phenomenon that occurs in arid, semi-arid and dry semi-wet regions, will reduce the land potential. In this study MEDALUS model was applied for quantitative assessment ...
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Nowadays, desertification as a great problem affects most of the countries in the world, especially developing countries. Desertification phenomenon that occurs in arid, semi-arid and dry semi-wet regions, will reduce the land potential. In this study MEDALUS model was applied for quantitative assessment of desertification risk with emphasis on five criterion in Shahroud-Bastam Basin, because of easy style and data accessibility and also compatible with the effective indices on land degradation. For this purpose, first, the study area was divided into five homogeneous units. Then, five criteria including soil quality, climate, vegetation, management, erosion and 13 indicators were used for quantitative evaluation of desertification in homogeneous units. Index layers for each criterion were prepared using GIS. These indices were ranked in accordance with Modified MEDALUS model. The geometric mean was then calculated and map was produced for each criterion. Land degradation map of each homogeneous unit was prepared using the geometric mean criteria. Finally, from their combination, desertification susceptibility map produced. According to the results, climatic and erosion criteria with an average weight of 1.607 and 1.467, had the most effectiveness on desertification and vegetation with average weight of 1.377 had the least effectiveness. As a results, from the total of study area, with area of 7737.71 km2, 40% (3096.311 km) is located in the C3 class or extreme critical erosion, that requiring more attention to implement desertification control programs. Result of this research is a managing tool available for decision-making regarding the selection of priority areas in the fight against desertification.
Alireza Arabameri; Kalil Rezaei; Mohammadhossein Ramshet; Kourosh Shirani
Abstract
Landslide susceptibility and its risk assessment is the main part of landslide risk mapping. In this study, landslide susceptibility of Oliya's Padena in Semirom is mapped using artificial neural network. A total of 23 factors in relation to landslide in the region were initially characterized. The spatial ...
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Landslide susceptibility and its risk assessment is the main part of landslide risk mapping. In this study, landslide susceptibility of Oliya's Padena in Semirom is mapped using artificial neural network. A total of 23 factors in relation to landslide in the region were initially characterized. The spatial location of landslide events was then determined by field study as well as aerial photo analysis. AHP analysis tends to 14 out of 23 parameters as the important factors for further steps. A total of 72 (70%) and 31 (30%) out of 103 detected landslide events in the study area were selected as training and validation data for neural network analysis, respectively. A multilayer perceptron back propagation algorithm with sigmoid as activation function was developed. The best topology was determined by using conventional criteria including mean square error, root mean square error, maximum absolute error and correlation coefficient. Results show that a 14-4-1 array is the optimum topology for landslide susceptibility zoning in the region. The weight of each input layer was estimated by frequency ratio. In order to map landslide, ROC graph and area under curve indices were used and the accuracy of output map was computed. Results from validation shows that area under curve for the obtained model is about 0.938 (93.8%) that is considered as high resolution prediction group. According to this study, a total of 29.61 square kilometers (93.25%) of the landslide areas is categorized in very high and high susceptible groups.
