This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. Received: OctoAccepted: JanuPublished: February 2, 2023Ĭopyright: © 2023 Li et al.

PLoS ONE 18(2):Įditor: Sher Muhammad, ICIMOD: International Centre for Integrated Mountain Development, NEPAL The comparison of the final evaluation results with those of other methods shows that the method proposed in this paper is a more reliable evaluation method for debris flow prevention and control.Ĭitation: Li L, Ni B, Qiang Y, Zhang S, Zhao D, Zhou L (2023) Risk assessment of debris flow disaster based on the cloud model-Probability fusion method. A total of 20 debris flow gullies were selected as study cases (8 debris flow gullies as model test, 12 debris flow gullies in reservoir area as example study). Through statistical analysis, this paper selects Debris flow scale (X 1), Basin area (X 2), Drainage density (X 3), Basin relative relief (X 4), Main channel length (X 5), Maximum rainfall (X 6) as evaluation indexes. Finally, a hierarchical comprehensive cloud is established by the Improving Cloud Model, which is used to input the comprehensive expectation composed of weights to obtain the risk level of debris flow.

Considering the uncertain characteristics of weights, the Monte Carlo Simulation is used to converge the weights in a minimal fuzzy interval, then the final weight value of each evaluation index is obtained. The new method tests the consistency of coupling weights according to the characteristics of the Cloud Model firstly, so as to determine the weight boundary of each evaluation index.

This paper proposes a new debris flow risk assessment method based on the Monte Carlo Simulation and an Improved Cloud Model.

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