Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

Jingbo Sun, Shengwu Qin (), Shuangshuang Qiao, Yang Chen, Gang Su, Qiushi Cheng, Yanqing Zhang and Xu Guo
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Jingbo Sun: Jilin University
Shengwu Qin: Jilin University
Shuangshuang Qiao: Jilin University
Yang Chen: Jilin University
Gang Su: Jilin University
Qiushi Cheng: Jilin University
Yanqing Zhang: Jilin University
Xu Guo: Jilin University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 106, issue 1, No 39, 912 pages

Abstract: Abstract Regional scale debris flow susceptibility is widely evaluated by statistical methods. However, the initiation mechanism of debris flow is not considered, which leads to the neglect of the microtopographic characteristics. To address this problem, we established three novel combined models by introducing the physical model into statistical methods. The integrating models consists of two parts, the statistical models and the TRIGRS model. The eventual results obtained with the integrating model consider both the prediction result of the statistical method for debris flow susceptibility and the mechanism of debris flow initiation. To test the feasibility of the integrating model, three representative statistical models, the analytic hierarchy process (AHP), Shannon entropy (Entropy) and support vector machine (SVM) were selected to evaluate debris flow susceptibility in Yongji County of Jilin Province, China. The results demonstrate that the performance of the integrated models is significantly better than that of the single statistical model, especially in the local areas. The integrating models (AHP-TR, Entropy-TR, SVM-TR) can generate higher quality debris flow susceptibility maps (DFSMs) than the single model, which clearly reflect the scope and boundaries of the areas which are most prone to debris flow and identify the flat land and valleys between adjacent high-prone areas. It also reduces the overprediction generated by the physical model. In general, combining the statistical methods with the TRIGRS model can maximize the strengths of these models and avoid their weaknesses and obtain the effect of 1 + 1 > 2.

Keywords: Integrating model; Statistical model; Physical model; Debris flow susceptibility mapping; Yongji County (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-020-04498-4

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