A spatiotemporal inference model for hazard chains based on weighted dynamic Bayesian networks for ground subsidence in mining areas

Ground subsidence concerns the long-term development of mining areas, and if not addressed effectively, it could gradually evolve into a major issue limiting the future economic development and survival of mining firms and local populations. However, there is unpredictability and uncertainty in the analysis of ground subsidence in mining areas, which is a quantitative and qualitative problem coupled with multiple indicators. By creating a chain relationship between ground subsidence in mining areas, this research provides a spatiotemporal inference model that integrates remote sensing (RS), geographic information system (GIS), and probabilistic map theory. The model uses a dynamic Bayesian framework to integrate the ground subsidence hazard chain in mining areas, standardizes multi-source data using GIS, computes node probabilities, and applies the entropy weight approach to improve model parameters. The Pingshuo mining area in China served as the study area for the model, and the mean values of area under the curve (AUC) and Brier score (BS) of the inferred results were 0.85 and 0.18, respectively, demonstrating that the model had some accuracy and dependability. Further analysis was performed on the impact of weights on the outcomes and the sensitivity of the model to the input nodes. The findings indicated that the spatiotemporal distribution of the results inferred from the model essentially matched the actual circumstance and could offer data assistance for mine safety management. The matching of the subsidence areas was effectively improved by optimizing the model with weights. The accuracy would also grow as the number of input nodes increased. The model proposed in this study is not limited by data, and the structure can be adjusted with the change of disaster chains, which is applicable to the study of multiple uncertainty problems.