جستجوی مقالات مرتبط با کلیدواژه "susceptibility" در نشریات گروه "آب و خاک"

Delineation of gully erosion susceptible areas by using statistical models, as well as optimum usage of existing data and information with the least time and cost and more precision, is important. The main objective of this study is to determine the areas accuracy to gully erosion and susceptibility mapping by using data mining of the bivariate Dempster-Shafer, linear multivariate statistical methods and their integration in Semirom watershed, southern Isfahan province. First, the geographical location of a total of 156 randomly gullies were mapped using preliminary reports, satellite imagery interpretation and field survey. In the next step, 14 conditioning parameters of the gullies in the study area were selected including the topographic, geomorphometric, environmental, and hydrologic parameters using the regional environmental characteristics and the multicollinearity test for modeling. Then, the Dempster-Shafer statistical, linear regression, and ensembled methods were developed using 70% of the identified gullies and 14 effective parameters as dependent and independent variables, respectively. The remaining 30% of the gully distribution dataset were used for validation. The results of the multivariate regression model showed that land use, slope and distance to drainage network parameters have the most significant relation to gully occurrence. The gully erosion susceptibility maps were prepared by individual and ensemble methods and they were divided to 5 classes of very low to very high rate. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate gully erosion susceptibly maps. The verification results showed that the AUC of ensemble method (0.948) is higher than Dempster-Shafer (0.924) and Multivariate regression (0.864) methods. Also, the the seed core area index (SCAI) value of the ensembled model from very low to very high susceptible classes have a decreasing trend that indicating a proper separation of susceptible classes by this model.

Shallow landslide is one of the natural hazards that damage life and property of people in mountainous watershed. Due to the fact that a lot of landslides events have been occurred in this watershed, assessment the risk of shallow landslides by using appropriate methods and determine of effective factors in reduce the hazards of it is so effective. The potential of using maximum entropy modeling for landslide susceptibility mapping is investigated. In the case study of west of Ardabil province, 74 landslide occurrences were identified, 52 landslides (70%) used for training and the 22 landslides (30%) applied for validation purpose. environmental factors including continuous (altitude, slope, aspect, plan curvature, drainage density, and rainfall) and categorical (lithology and landuse) data were used as inputs for modeling. From the optimal setting test based on cross-validation, a continuous data and its combination with categorical data showed the best predictive performance. The results of validation showed that the ROC and AUC for success and prediction rate of model was 96.1 and 97.6%, respectively. Factor contribution analysis indicated that altitude and rainfall layers were the most influential factors. From interpretations on a response curve, steeply sloping areas that consisted of excessively covered with old alluvial terrace soils were very susceptible to landslides. Predictive performance of maximum entropy modeling was slightly better than that other models like of a logistic regression which has been used widely to assess landslide susceptibility. Therefore, Maximum entropy modeling is shown to be an effective prediction model for landslide susceptibility mapping.