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.

将坡度单位作为我们的参考映射单位，将三种统计模型[频率比（FR），熵指数（IOE）和证据置信函数（EBF）]通过两种方法结合使用[归一化空间相关比例指数（ NSCI）和加权标定滑坡潜在模型（WCLPM）]。为此，考虑了与滑坡相关的十个调节因素，即海拔，坡度，坡度，起伏幅度，开挖深度，沟壑密度，表面粗糙度，到道路的距离，降雨和岩性。使用接收器工作特性（ROC）曲线（AUC）下的面积和几种统计评估方法来测试模型的性能。基于加权校正滑坡潜在指数（WCLPI）的FR模型具有最高的预测能力，其次是基于校正后的滑坡潜力指数（CLPI）的FR，WCLPI-EBF，CLPI-EBF，WCLPI-IOE，CLPI-IOE，FR，EBF和IOE模型。结果表明，混合模型显着改善了单个模型的性能。这突出表明，NSCI和WCLPM混合技术是用于滑坡敏感性评估的有前途的方法。