A Bayesian network-based risk assessment model for seismic landslide disaster chains
JIANG Shangwei1, 2, FU Xiaodong1, 2*, KANG Jingyu1, 2, DING Haifeng1, 2
(1. State Key Laboratory of Geomechanics and Geotechnical Engineering Safety, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China)
Abstract：The seismic landslide hazard chain encompasses various physical processes, including landslide initiation, slope movement, river damming, and dam-break flooding. It is characterized by multifactorial influences, the coupling of multiple hazards, and an interconnected sequence of disaster events, highlighting the need for a comprehensive risk assessment model. This paper develops a risk assessment model for the seismic landslide hazard chain based on Bayesian theory. Initially, the primary nodes of the Bayesian network were identified through bibliometric mining combined with multi-source data. Subsequently, by integrating a database of typical seismic landslide hazard chain cases, an initial Bayesian network model was constructed using the Expectation-Maximization algorithm. A more refined coupled model with enhanced nodes was then developed by incorporating the cascade mechanisms among various hazards. Finally, using the Brier score criterion and taking the Mogangling landslide hazard chain as a case study, the predictive capabilities of both models were evaluated by calculating the mean squared error between predicted probabilities and actual hazard occurrences. The results indicate that: (1) Predictions of landslide volume, dammed lake capacity, and barrier dam stability from both the initial and coupled models are consistent with field observations. The selection of network nodes, determination of structure, and the use of posterior probabilities derived from parameter learning as prior probabilities for the predictive model effectively replace subjective expert experience, thereby enhancing the reliability of holistic risk assessments for seismic hazard chains. (2) The Brier score of the coupled model reached 0.146 7, outperforming the original model?s score of 0.242 7, demonstrating that the multi-hazard coupling method improves the predictive performance of the Bayesian network model. (3) The multi-hazard coupled model significantly reduces the Brier score by enhancing the efficiency of available data utilization, thereby addressing the issue of data scarcity in parameter learning across the entire seismic landslide hazard chain process. This study offers novel insights and decision support for hazard chain prediction and disaster mitigation through chain-breaking strategies.
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2026, Vol. 45 
