Abstract
Along mountain roads, rainfall-triggered landslides are typical disasters that cause significant human casualties. Thus, to establish effective mitigation measures, it would be very useful were government agencies and practicing land-use planners to have the capability to make an accurate landslide evaluation. Here, we propose a machine learning methodology for the spatial prediction of rainfall-induced landslides along mountain roads which is based on a random forest classifier (RFC) and a GIS-based dataset. The RFC is used as a supervised learning technique to generalize the classification boundary that separates the input information of ten landslide conditioning factors (slope, aspect, relief amplitude, toposhape, topographic wetness index, distance to roads, distance to rivers, lithology, distance to faults, and rainfall) into two distinctive class labels: ‘landslide’ and ‘non-landslide’. Experimental results with a cross validation process and sensitivity analysis on the RFC model parameters reveal that the proposed model achieves a superior prediction accuracy with an area under the curve of 0.92. The RFC significantly outperforms other benchmarking methods, including discriminant analysis, logistic regression, artificial neural networks, relevance vector machines, and support vector machines. Based on our experimental outcome and comparative analysis, we strongly recommend the RFC as a very capable tool for spatial modeling of rainfall-induced landslides.
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Acknowledgements
Data for this research are from the project 71 /GV-VKHĐCKS with the title “Combination of Structural Geology, Remote Sensing, and GIS for the Study of Current Status and Prediction of Flash Floods and Landslides at the National Road No.32 Section from the Yen Bai to the Lai Chau Provinces”, Vietnam Institude of Geosciences and Mineral Resources. We would like to thank Dr. Ho Tien Chung for providing the data for this research.
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Dang, VH., Dieu, T.B., Tran, XL. et al. Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bull Eng Geol Environ 78, 2835–2849 (2019). https://doi.org/10.1007/s10064-018-1273-y
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DOI: https://doi.org/10.1007/s10064-018-1273-y