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Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.

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Landslide susceptibility assessment is an important support for disaster identification and risk management. This study aims to analyze the application ability of machine learning hybrid models in different evaluation units. Three typical machine learning models, including random forest forest by penalizing attributes (FPA) and rotation forest were merged by random subspace algorithm. Twelve evaluation factors, including elevation, slope angle, slope aspect, roughness, rainfall, lithology, distance to rivers, distance to roads, normalized difference vegetation index, topographic wetness index, plan curvature, and profile curvature, were extracted from 155 landslides in Yaozhou District, Tongchuan City, China. Six landslide susceptibility maps were generated based on the slope units divided by curvature and 30 m resolution grid units. Multiple performance metrics showed that the RS-RF model based on slope units has excellent spatial prediction ability. At the same time, the method of slope unit division based on curvature is proved to be more suitable for the typical Loess tableland regions, which provides basis for the selection of evaluation units in landslide susceptibility assessment.