The success rates with the training dataset were 0.880, 0.877 and 0.886 for the EBF, LADTree and LMT models, respectively. Finally, the receiver operating characteristic (ROC) curve was utilized to contrast and test the capacity of the three models. The following procedure was to map landslide susceptible regions through EBF, LADTree and LMT models. For the study area, 12 landslide-related conditioning factors were identified, for instance, plan curvature, profile curvature, elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use, lithology, distance to river soil, and distance to roads. Then, 70% of landslide points were used as training samples randomly, and the remaining 30% were intended for validation samples. Firstly, 328 landslides were mapped in the study area. The primary objective of the present research is to apply and compare the performance of evidential belief function (EBF)-based logistic model trees (LMTs) and multiclass alternate decision trees (LADTrees) in landslide susceptibility mapping in Xiaojin County, China. This study’s approach also contributes to developing a suitable RWH identification methodology, especially for dry regions in Indonesia. The initial identification of RWH potential sites could be valuable information in completing water conservation programs for several purposes. These areas are characterized by dryland farming as the dominant land use, gentle slope, high runoff potential, high drainage density, and moderately fine soil texture. The result also indicated that approximately 38% of the Nusa Penida Island is highly suited for RWH. This study’s parameters and hybrid method were effective tools for identifying RWH suitable sites. Biophysical (slope, soil texture, drainage density, land use), hydrological (runoff potential), and socio-economic (distance to road, distance to river, distance to settlement) parameters of the study area were implemented integrating multi-criteria decision analysis (analytical hierarchy processes) and Geographic Information System (GIS) to evaluate RWH suitable sites in Nusa Penida Island, Indonesia. Identifying RWH suitable sites is site-specific due to a wide variety of a region’s characteristics. The critical step in increasing water availability and land productivity in areas with freshwater scarcity, such as arid and semi-arid, is identifying suitable sites for Rain Water Harvesting (RWH).
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