Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

Jie Dou (), Ali P. Yunus (), Yueren Xu (), Zhongfan Zhu (), Chi-Wen Chen (), Mehebub Sahana (), Khabat Khosravi (), Yong Yang () and Binh Thai Pham ()
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Jie Dou: Ministry of Education
Ali P. Yunus: Aligarh Muslim University
Yueren Xu: Institute of Earthquake Forecasting, China Earthquake Administration
Zhongfan Zhu: Beijing Normal University
Chi-Wen Chen: National Science and Technology Center for Disaster Reduction
Mehebub Sahana: Jamia Millia Islamia
Khabat Khosravi: The University of Tokyo
Yong Yang: Sari Agricultural Science and Natural Resources University
Binh Thai Pham: Duy Tan University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 97, issue 2, No 8, 579-609

Abstract: Abstract This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

Keywords: Shallow landslide; Certainty factor; Binary logistic regression; Torrential rainfall; Typhoon Bilis (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-019-03659-4

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