Dynamic Grey Correlation Model for Debris Flow Risk Assessment and Its Application: A Case Study of Western Niushou Mountain

Jia-wei LIU , Feng-chuan CHEN , Gao LIU , Yao-dong WU , Shu-chen XUAN , Fei YU

Science Technology and Engineering ›› 2024, Vol. 24 ›› Issue (33) : 14129 -14135.

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Science Technology and Engineering ›› 2024, Vol. 24 ›› Issue (33) : 14129-14135 DOI: 10.12404/j.issn.1671-1815.2401192
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Dynamic Grey Correlation Model for Debris Flow Risk Assessment and Its Application: A Case Study of Western Niushou Mountain
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Abstract

In order to objectively determine the weight of debris flow impact factors and accurately and reasonably evaluate the risk of debris flow, a dynamic grey correlation model for debris flow risk was constructed. Aiming at the limitation of factor weight determination in traditional grey correlation analysis method, combined with triangular fuzzy theory, a dynamic resolution coefficient was proposed. According to the actual situation of different debris flow gullies, the identification coefficient of each factor was dynamically adjusted and the corresponding weight was determined, and then a dynamic grey correlation model for debris flow risk was established. The model was used to evaluate the risk of seven typical debris flow gullies in the western Niushou Mountain and compared with the results of other evaluation methods. The results show that the dynamic grey correlation model of debris flow risk can expand the discrimination of debris flow risk assessment results and improve the resolution because of the dynamic resolution considering the actual situation and factor characteristics of each gully and calculating the factor weight accordingly. The evaluation results are more consistent with the actual situation.

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debris flow / grey theory model / fuzzy theory / dynamic identification coefficient / risk assessment

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Jia-wei LIU, Feng-chuan CHEN, Gao LIU, Yao-dong WU, Shu-chen XUAN, Fei YU. Dynamic Grey Correlation Model for Debris Flow Risk Assessment and Its Application: A Case Study of Western Niushou Mountain. Science Technology and Engineering, 2024, 24(33): 14129-14135 DOI:10.12404/j.issn.1671-1815.2401192

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