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Geological disasters, particularly landslides and rockfalls, cause significant casualties and losses in Southwest China's mountains. Their occurrence stems from multiple factors, making multi-source data integration crucial for effective monitoring and early warning. This paper reviews real-scene 3D modeling and machine learning foundations for disaster management. Using the Fuling District, Chongqing case study, it demonstrates the critical role of integrating environmental, meteorological, and hydrogeological monitoring data for intelligent early warning. The goal is to determine slope stability via monitoring, enabling timely warnings and effective landslide prevention.

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Intelligent Early Warning and Prevention for Geological Hazards Based on Multi-Source Monitoring Data Fusion with 3D Real Scene Technology and Machine Learning: A Case Study of Fuling District, Chongqing

  • 会议论文

  • 发表日期：

    2026年

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  • Pengfei Chen,
  • Shang Huang,
  • Qi Wang,
  • Zhongxin Zhou,
  • Yu He

摘要

Geological disasters, particularly landslides and rockfalls, cause significant casualties and losses in Southwest China's mountains. Their occurrence stems from multiple factors, making multi-source data integration crucial for effective monitoring and early warning. This paper reviews real-scene 3D modeling and machine learning foundations for disaster management. Using the Fuling District, Chongqing case study, it demonstrates the critical role of integrating environmental, meteorological, and hydrogeological monitoring data for intelligent early warning. The goal is to determine slope stability via monitoring, enabling timely warnings and effective landslide prevention.