Abstract
This study selected ten condition factors from the perspectives of topography, geological structure, hydrology and human for mining subsidence mapping by two types of models (knowledge-driven model and data-driven model) by taking Jining City, China as an example. The knowledge-driven model is achieved by analytic hierarchy process (AHP), and the data-driven model is achieved by frequency ratio (FR), combined with fuzzy comprehensive evaluation (FCE) and entropy evaluation method (EWM). Based on these models, the areas under receiver operating characteristic curve are 0.777, 0.949, and 0.938, which means FR-FCE obtained the highest accuracy. The differences reflected by the three methods are concentrated in Liangshan County and central to northern areas of Weishan County. The best model was proposed for the protection measures and land use management. Finally, the factors system and methods used in this study can be widely applied to the mining subsidence susceptibility evaluation in plain areas.
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