Abstract
Numerous researches have been published on the application of landslide susceptibility assessment models; however, they were only applied in the same areas as the models were originated, the effect of applying the models to other areas than the origin of the models has not been explored. This study is purposed to develop an optimized random forest (RF) model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping (LSM), and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model. Two typical counties (Fengjie County and Wushan County) in the Three Gorges Reservoir area, China, which have the same terrain and geological conditions, were selected as an example. To begin with, landslide inventory was prepared based on field investigations, satellite images, and historical records, and 1 522 landslides were then identified in Fengjie County. 22 landslide-conditioning factors under the influence of topography, geology, environmental conditions, and human activities were prepared. Then, combined with 10-fold cross-validation, three typical ratios of positive-to-negative cells, i.e., 1:1, 1:5, and 1:10, were adopted for comparative analyses. An optimized RF model (Fengjie-based model) with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed. Finally, the Fengjie-based model was applied to Fengjie County and Wushan County, and the confusion matrix and area under the receiver operating characteristic (ROC) curve value (AUC) were used to estimate the accuracy. The Fengjie-based model delivered high stability and predictive capability in Fengjie County, indicating a great generalization ability of the model to the area where the model is originated. The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value, indicating the Fengjie-based model had a great generalization ability in area other than the origin of the model. The Fengjie-based model in this study could be applied in other similar areas/countries with the same terrain and geological conditions, and a LSM may be generated without collecting landslide information for modeling, so as to reduce workload and improve efficiency in practice.
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Acknowledgments
We want to express our gratitude to Chongqing Meteorological Administration for providing essential meteorological data and also to Chongqing Institute of Geology and Mineral Resources for offering valuable research data on historical landslides. We are also grateful to the editors and anonymous reviewers for their valuable comments on this manuscript. This study was supported by the National Natural Science Foundation of China (No. 41807498), the National Key Research and Development Program of China (No. 2018YFC1505501), and the Humanities and Social Sciences Foundation of the Ministry of Education of China (No. 20XJAZH002). The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1072-9.
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Sun, D., Xu, J., Wen, H. et al. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China. J. Earth Sci. 31, 1068–1086 (2020). https://doi.org/10.1007/s12583-020-1072-9
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DOI: https://doi.org/10.1007/s12583-020-1072-9