Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold：A case study from Fuling District, Chongqing

PENG Shuangqing; LIU Pengfei; CHEN Gang; WANG Liping; ZHANG Wei; LUO Wenwen; JING Xiliang

doi:10.16031/j.cnki.issn.1003-8035.202402015

2025 Vol. 36, No. 1

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PENG Shuangqing, LIU Pengfei, CHEN Gang, WANG Liping, ZHANG Wei, LUO Wenwen, JING Xiliang. Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold：A case study from Fuling District, Chongqing[J]. The Chinese Journal of Geological Hazard and Control, 2025, 36(1): 131-145. doi: 10.16031/j.cnki.issn.1003-8035.202402015

Citation:

PENG Shuangqing, LIU Pengfei, CHEN Gang, WANG Liping, ZHANG Wei, LUO Wenwen, JING Xiliang. Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold：A case study from Fuling District, Chongqing[J]. The Chinese Journal of Geological Hazard and Control, 2025, 36(1): 131-145. doi: 10.16031/j.cnki.issn.1003-8035.202402015

Regional landslide hazard assessment using the IV-RF coupling model and critical monthly average rainfall threshold：A case study from Fuling District, Chongqing

1.
Chongqing 208 Institute of Geological Environment Co. Ltd., Chongqing　400700, China
2.
Chongqing Institute of Geological Environment Monitoring, Chongqing　401120, China
3.
Nanjiang Hydro-Geology and Engineering Geology Team of Chongqing Geology Mineral Bureau, Chongqing,　401120, China
4.
School of Civil Engineering and Architecture, Chongqing University of Science and Technology, Chongqing,　401331, China

More Information

Corresponding author: LIU Pengfei, 273888264@qq.com

Received Date: 26 February 2024

Revised Date: 28 May 2024

Accepted Date: 03 July 2024

Publish Date: 25 February 2025

Abstract

Abstract

Improving the accuracy of susceptibility prediction for rainfall-induced landslides and establishing suitable rainfall threshold models are of great significance for regional landslide hazard assessment. Taking Fuling District of Chongqing as a case study, the information value model, BP neural network model, random forest model, information value-BP neural network coupled model, and information value-random forest coupled model were used to evaluate regional landslide susceptibility. By comparing the receiver operating characteristic (ROC) curves, area under the curve (AUC), and susceptibility distribution patterns of different models, a critical monthly average rainfall threshold model for landslides is proposed, and critical monthly average rainfall thresholds for different temporal probabilities were inferred. The susceptibility results were coupled with temporal probability levels to produce regional landslide hazard assessment results. The evaluation accuracy is further validated with 30 randomly selected landslide events and 4 typical landslide cases. The results show that coupling the Information Value and machine learning models compensates for the shortcomings of machine learning in early data input and non-sample selection, enhancing the predictive accuracy of single machine learning models. Among these, the information value-random forest coupled model exhibits the highest predictive accuracy; of the 30 randomly selected landslide samples, 20 cases (67%) occurred in areas with a temporal probability of over 50%, validating the accuracy of the critical monthly average rainfall threshold model. The 4 typical landslide samples selected randomly were primarily in the P4 or P5 temporal probability levels and were located in high to very high-risk areas, aligning well with field survey results. This indicates that the regional landslide hazard assessment based on the information value-random forest coupled model and the critical monthly average rainfall threshold is accurate and reliable.
- landslide hazard /
- hazard assessment /
- IV-RF coupled model /
- critical monthly average rainfall /
- disaster prevention and mitigation

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References

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