期刊
ENVIRONMENTAL EARTH SCIENCES
卷 80, 期 8, 页码 -出版社
SPRINGER
DOI: 10.1007/s12665-021-09603-9
关键词
Landslide susceptibility; Evidential belief function; Frequency ratio; Index of entropy; Normalized spatial-correlated scale index; Weighted calibrated landslide potential model
资金
- China Postdoctoral Science Foundation [2020M680583]
- National Postdoctoral Program for Innovative Talent of China [BX20200191]
- Shuimu Tsinghua Scholar Program [2019SM058]
- Excellent Sino-foreign Youth Exchange Program of China Association for Science and Technology in 2020 [58]
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.
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.
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