Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model

Shaohan Zhang, Shucheng Tan (), Jinxuan Zhou, Yongqi Sun, Duanyu Ding and Jun Li
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Shaohan Zhang: Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
Shucheng Tan: Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
Jinxuan Zhou: Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
Yongqi Sun: Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
Duanyu Ding: Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China
Jun Li: Yunnan Architectural Engineering Design Company Limited, Kunming 650501, China

Sustainability, 2023, vol. 15, issue 17, 1-21

Abstract: An assessment of regional vulnerability to geological disasters can directly indicate the extent and intensity of risks within the study area; thus, providing precise guidance for disaster management efforts. However, in the evaluation of geological disaster susceptibility using a single deterministic coefficient model, the direct superimposition of deterministic coefficient values for each evaluation factor, without considering their objective weights, can impact the accuracy of susceptibility zoning outcomes. To address this limitation, this research proposes a novel approach: geological disaster susceptibility evaluation using a random-forest-weighted deterministic coefficient model. In this method, the objective weight of each evaluation factor is calculated based on a deterministic coefficient model and a parameter-optimized random forest model. By weighting and superimposing the deterministic coefficient values of each evaluation factor, a comprehensive deterministic coefficient map is generated. This map is further divided using the natural breakpoint method to obtain a geological disaster susceptibility zoning map. To validate the accuracy of the evaluation results, partition statistics and the ROC (Receiver Operating Characteristic) curve of the test sample points are utilized. The findings demonstrate that the model performs well in evaluating geological disaster susceptibility in Huize County. The evaluation results are considered reliable and accurate, highlighting the effectiveness of the proposed approach for assessing and zoning geological disaster susceptibility in the region.

Keywords: geological hazard; susceptibility; random forests; certainty factor; Huize County (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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