Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example

Ming Li, Linlong Li, Yangqi Lai, Li He (), Zhengwei He and Zhifei Wang
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Ming Li: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
Linlong Li: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
Yangqi Lai: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
Li He: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
Zhengwei He: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China
Zhifei Wang: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu 610059, China

Sustainability, 2023, vol. 15, issue 14, 1-21

Abstract: The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.

Keywords: Zemu River valley; geological hazards; machine learning; susceptibility (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 (2)

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