Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model

Considering the slope units as our reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)]. For this aim, ten conditioning factors correlated with landslide namely, altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, surface roughness, distance to roads, rainfall, and lithology are considered. The performance of the models is tested using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical evaluation measures. The weighted calibrated landslide potential index (WCLPI)-based FR model has the highest predictive capability, followed by the calibrated landslide potential index (CLPI)-based FR, the WCLPI-EBF, the CLPI-EBF, the WCLPI-IOE, the CLPI-IOE, the FR, the EBF, and the IOE models, respectively. Results indicated that hybrid models have improved significantly the performance of single models. This highlights that NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.

Automated summary

Three statistical models and two methods were used to analyze ten conditioning factors related to landslides, with the hybrid models showing significantly improved predictive capability compared to single models. The NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.