Hamid Reza Pourghasemi
Articles written in Journal of Earth System Science
Volume 122 Issue 2 April 2013 pp 349-369
The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning.
Volume 126 Issue 8 December 2017 Article ID 0109
Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster–Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.