Abstract
Landslide susceptibility is the likelihood of a landslide occurring in an area. The logistic regression (LR) method is one of the most popular methods for landslide susceptibility assessment. For rainfall-induced landslides, yearly or monthly rainfall is commonly used to establish a landslide susceptibility model by the LR method. It is a static susceptibility model, which limits the application to predict future landslide probability under potential rainfall event. This study presents a revised logistic regression method to achieve dynamic landslide susceptibility prediction under cumulative daily rainfall. Five kinds of cumulative daily rainfall are used in the landslide susceptibility assessment. The latest landslide events are used to update the landslide susceptibility model. The receiver operation characteristic curve and area under curve are utilized to evaluate the prediction reliability. The landslide susceptibility assessment in Shenzhen is taken as an illustration of the proposed method. The result indicates the method is capable to achieve a high accuracy of 91.9% when the landslide susceptibility model is updated using seven extreme rainfall events in the past 10 years. This method provides an advance prediction of the potential geo-hazards for a large area using the future rainfall forecast.
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Acknowledgements
This work was supported by Shenzhen Science and Technology Research& Development Fund (JCYJ20180507183854827), National Natural Science Foundation of China (No. 51909288) and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). We are grateful to Planning and Natural Bureau and Meteorological Bureau of Shenzhen Municipality for providing landslide and rainfall data, respectively, in this study. Great thanks to Dr. Qianzhu Zhang and Ms. Jianmei Yan of Changjiang River Scientific Research Institute (CRSRI) for the help with the GIS software operation.
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Xing, X., Wu, C., Li, J. et al. Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method. Nat Hazards 106, 97–117 (2021). https://doi.org/10.1007/s11069-020-04452-4
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DOI: https://doi.org/10.1007/s11069-020-04452-4