Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Zhiheng Wang (), Dongchuan Wang, Qiaozhen Guo and Daikun Wang ()
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Zhiheng Wang: Tianjin Chengjian University
Dongchuan Wang: Tianjin Chengjian University
Qiaozhen Guo: Tianjin Chengjian University
Daikun Wang: The Chinese University of Hong Kong

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 104, issue 3, No 10, 2153-2173

Abstract: Abstract Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

Keywords: Landslides; Hazard assessment; Susceptibility influence; Logistic regression (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-04265-5

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