Abstract
Landslide is considered one of the most dangerous natural hazards. Reasonable landslide susceptibility mapping can aid decision makers in landslide prevention. For this reason, based on the field survey data of landslide in Chenggu County, Shaanxi Province, China, 15 conditioning factors (altitude, slope, aspect, plan curvature, profile curvature, SPI, TWI, distance to roads, distance to rivers, distance to faults, rainfall, NDVI, soil, lithology, and land use) were selected and quantified by the certainty factor index. Then, 184 landslides data were divided into training and validation datasets according to the ratio of 7/3. Based on the GIS platform, three hybrid tree-based models, namely decision tree (DT), logistic model tree (LMT), and reduced error pruning tree (REPT), were established. Additionally, the bagging method was applied to build three bag-hybrid tree-based models: Bag-DT, Bag-LMT, and Bag-REPT. Finally, the landslide susceptibility maps were produced, and statistical indexes, seed cell area index and the ROC curve, were used for model validation and comparison. The results showed that the bagging method can significantly improve the classification ability of hybrid models. Furthermore, the Bag-REPT presented the best performance, with an accuracy value of 92.5%, being a suitable model for landslide susceptibility mapping in the study area.
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References
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This study is funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES) (Finance Code 001), Opening Fund of Key Laboratory of Land Remediation of Shaanxi Province (300102351502), National Natural Science Foundation of China (211035210511), Internal scientific research project of Shaanxi Land Engineering Construction Group (DJNY2021-10) and Shaanxi Province Natural Science Basic Research Project (2021JQ-961).
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Tingyu Zhang done conceptualization; Tingyu Zhang and Renata performed methodology, writing—original draft preparation, and funding acquisition; Dan Luo contributed to software; Tao Wang validated the study; Renata done formal analysis and writing—review and editing; Huanyuan Wang investigated the study;Quan Fu searched resources; Guilherme Garcia de Oliveira done data curation;;Laurindo Antonio Guasselli visualized the study; Huanyuan Wang done supervision; Camilo Daleles Renno was involved in project administration;.
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Zhang, T., Quevedo, R., Wang, H. et al. Improved tree-based machine learning algorithms combining with bagging strategy for landslide susceptibility modeling. Arab J Geosci 15, 183 (2022). https://doi.org/10.1007/s12517-022-09488-3
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DOI: https://doi.org/10.1007/s12517-022-09488-3