Research on Land Subsidence Prediction in Mining Areas Based on SBAS-InSAR and Multi-Model Comparison

Authors

  • Qingkun Yang College of Construction Engineering, Jilin University, Changchun 130012, Jilin, China
  • Peihua Xu College of Construction Engineering, Jilin University, Changchun 130012, Jilin, China
  • Chen Cao Northeast Electric Power Design Institute, Changchun 130021, Jilin, China
  • Bo Shan Northeast Electric Power Design Institute, Changchun 130021, Jilin, China
  • Yimin Liu Northeast Electric Power Design Institute, Changchun 130021, Jilin, China
  • Tie Jin Geological Survey Academy of Inner Mongolia Autonomous Region, Hohhot 010020, Inner Mongolia, China
  • Xiguan An Geological Survey Academy of Inner Mongolia Autonomous Region, Hohhot 010020, Inner Mongolia, China

DOI:

https://doi.org/10.53469/jrse.2024.06(12).11

Keywords:

Mining Areas, Land Subsidence, Multi-Model Comparison, PSO-SVR, Prediction Analysis

Abstract

Land subsidence in mining areas caused by underground resource exploitation poses a serious threat to surface stability and ecological security. Accurate prediction of land subsidence is crucial for disaster prevention and mitigation in mining areas. This study integrates SBAS-InSAR technology with various predictive models to analyze and forecast surface subsidence in mining areas. First, SBAS-InSAR technology is used to process Sentinel-1 data from 2018 to 2023, extracting time-series deformation data in the study area. Then, based on the key influencing factors of land subsidence, a comparative analysis of multiple models, including SVR, PSO-SVR, and HOLT, is conducted to assess their performance in subsidence prediction. The results show that the PSO-SVR model, optimized with particle swarm optimization, demonstrates superior accuracy and reliability compared to the other models. This provides a robust approach for monitoring and predicting land subsidence in mining areas, offering technical support for risk management and sustainable resource development.

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Published

2024-12-26

How to Cite

Yang, Q., Xu, P., Cao, C., Shan, B., Liu, Y., Jin, T., & An, X. (2024). Research on Land Subsidence Prediction in Mining Areas Based on SBAS-InSAR and Multi-Model Comparison. Journal of Research in Science and Engineering, 6(12), 63–71. https://doi.org/10.53469/jrse.2024.06(12).11

Issue

Vol. 6 No. 12 (2024): JRSE-Volume 6 Issue 12

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Articles

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Copyright (c) 2024 Qingkun Yang, Peihua Xu, Chen Cao, Bo Shan, Yimin Liu, Tie Jin, Xiguan An

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.