Haji Karimi; Fathollah Naderi; Behrooz Naseri; Ali Salajeqeh
Abstract
Distinguishing the susceptible areas to landslide using different landslide susceptibility mapping (LSM) models is one of the primitive and basic works to reduce probable damages and reduce risk. The main purpose of this research is the efficiency evaluation of four methods including Information value ...
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Distinguishing the susceptible areas to landslide using different landslide susceptibility mapping (LSM) models is one of the primitive and basic works to reduce probable damages and reduce risk. The main purpose of this research is the efficiency evaluation of four methods including Information value (WINF), Valuing area accumulation (Wa), Analytical Hierarchy Process (AHP), Kopta-Joshi proposed method (LNRF) for LSM in Zangvan watershed, Ilam province. At first, all the effective factors in landslide occurrence were inspected. By analyzing the parameters, nine factors including slope, aspect, elevation, precipitation, distance from road, distance from fault, distance from drainage, land use and lithology were distinguished as the effective factors in landslides occurrence in the studied area. After preparing the information of these nine factors in GIS environment, the location of landslides were determined using areal photographs and satellite images and LSM performed by the above four methods. Finally, the landslide index was used for evaluation the ability of appropriate LSM model. Based on this Index, the information value method classified more 52 percent of occurred landslides in very high danger class. Therefore, this method is more efficient and proposed as the best LSM method in the Zangvan watershed because of compatibility of landslides with high danger classes and ability of differentiation of danger classes.
Maryam Khosravi; Ali Salajegheh; Mohammad Mahdavi; Mohsen Mohseni Saravi
Abstract
It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan ...
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It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan watershed. Maximum daily mean discharge and corresponding daily rainfall, one day antecedent and five days antecedent rainfall, sum of five days antecedent rainfall and monthly mean temperature were extracted in Gatehdeh, Mehran, Alizan, Joestan and Gelinak hydrological units and entered into neural network model (from upstream to downstream, respectively). The feed forward network was used with one hidden layer and back-propagation algorithm. Then, the models were trained, validated and tested in three stages. The observed and estimated peak discharges of the models were compared based on RMSE and r. The results showed that neural network has better performance than nonlinear multivariate regression.
