Landslide susceptibility mapping using a Matrix Exponential Spatial Specification model: a case study in Semirom, Isfahan, Iran

The main objective of the current study is to apply Matrix Exponential Spatial Specification (MESS) model and Multivariate Linear Regression (MLR) model to compare their accuracy and performance in landslide susceptibility mapping. In this study, 11 information layers including slope, aspect, plan curvature, profile curvature, distance from faults, distance from residential areas, distance from roads, distance from rivers, lithology, land use and rainfall have been used as conditioning factors in landslide occurrence in the study area. For this purpose, at first, a total of 68 occurred landslides were identified in the study area using interpretation of aerial photographs and field surveys. Using the aforementioned algorithms, landslide-susceptible areas were prepared for the study area. The accuracy of the prepared models was evaluated using the Receiver Operating Characteristic (ROC) curve. The coefficient of determination (?2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for aforementioned methods. The consequences presented that the MESS model exhibited the highest ?2 (0.8823), followed by MLR model (0.4852). Similarly, the ROC plots and RMSEs also showed that the prediction rates gave similar results. Therefore, it can be concluded that in this study the MESS model exhibiting the best performance in landslide susceptibility mapping.

Keywords:

Landslide , Matrix Exponential Spatial Specification model , Multivariate Linear Regression