Dynamic Evaluation of Disaster Reduction Capacity in Mining Cities Based on Big Data 

Wei Chunyu

Henan Polytechnic University, Jiaozuo Henan 454003;

Abstract:Mining cities play a critical role in resource extraction and environmental conservation; enhancing disaster resilience in these cities is essential for ensuring regional sustainable development and safeguarding both human lives and ecological systems. Leveraging the multi-source integration, real-time acquisition, and powerful analytical capabilities of big data technology, this paper elaborates on the theoretical framework and practical significance of dynamic disaster reduction capacity assessment specifically tailored for mining cities. A four-level evaluation framework comprising the data layer, indicator layer, model layer, and application layer was established to systematically integrate multi-dimensional data encompassing geological environmental elements, building structures, socioeconomic factors, and social governance parameters. By combining the analytic hierarchy process (AHP) with entropy weighting and subsequently introducing a dynamic adjustment factor, the proposed model effectively captures both the temporal evolution and spatial variation characteristics of disaster reduction factors. The model was validated using empirical data from representative coal mining cities, and three-dimensional dynamic visualization charts together with associated analytical datasets were employed to identify key influencing factors and their transformation patterns, thereby providing scientifically rigorous evidence to support future disaster management policy formulation and implementation.

Keywords:big data; mining cities; disaster reduction capacity; dynamic evaluation

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