Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran

Landslide is one of the most important natural hazards that make numerous financial damages and life losses each year in the worldwide. Identifying the susceptible areas and prioritizing them in order to provide an efficient susceptibility management is very vital. In current study, a comparative analysis was made between combined bivariate and AHP models (bivariate-AHP) with a logistic regression. At first, landslide inventory map of the study area was prepared using extensive field surveys and aerial photographs interpretation. In the next step, nine landslide causative factors were selected including altitude, slope percentage, slope aspect, lithology, distance from faults, streams and roads, land use, and precipitation which affect occurrence of the landslides in the study area. Subsequently, landslide susceptibility maps were produced using weighted (AHP) bivariate and logistic regression models. Finally, receiver operating characteristics (ROC) curve was used in order to evaluate the prediction capability of the mentioned models for landslide susceptibility mapping. According to the results, the combined bivariate and AHP models provided slightly higher prediction accuracy than logistic regression model. The combined bivariate and AHP, and logistic regression models had the area under the curve (AUC-ROC) values of 0.914, and 0.865, respectively. The resultant landslide susceptibility maps can be useful in appropriate watershed management practices and for sustainable development in the regions with similar conditions

Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models

Modelling the flood in watersheds and reducing the damages caused by this natural disaster is one of the primary objectives of watershed management. This study aims to investigate the application of the frequency ratio and maximum entropy models for flood susceptibility mapping in the Madarsoo watershed, Golestan Province, Iran. Based on the maximum entropy and frequency ratio methods as well as analysis of the relationship between the flood events belonging to training group and the factors affecting on the risk of flooding, the weight of classes of each factor was determined in a GIS environment. Finally, prediction map of flooding potential was validated using receiver operating characteristic (ROC) curve method. ROC curve estimated the area under the curve for frequency ratio and the maximum entropy models as 74.3% and 92.6%, respectively, indicating that the maximum entropy model led to better results for evaluating flooding potential in the study area