STEMM Institute Press
Science, Technology, Engineering, Management and Medicine
Application and Analysis of GIS Technology in Geological Hazard Assessment
DOI: https://doi.org/10.62517/jes.202502310
Author(s)
Wanwan Zhao, Qiyue Feng
Affiliation(s)
Shanxi Institute of Geological Survey Co., Ltd., Taiyuan, Shanxi, China
Abstract
This paper systematically examines the theoretical significance, technical methodologies and practical applications of Geographic Information Systems (GIS) in geological hazard assessment, with particular emphasis on their role in evaluating typical hazards such as landslides, earthquakes and debris flows. Through spatial data collection, modelling, and analysis, GIS integrates multi-source data including remote sensing imagery, geological surveys, and sensor monitoring to construct hazard susceptibility zoning and risk prediction models, significantly enhancing evaluation precision and operational feasibility. Research indicates that GIS techniques—spatial overlay analysis, proximity analysis, and three-dimensional visualisation—effectively reveal spatial distribution patterns and evolutionary processes of disasters, providing scientific foundations for disaster prevention and mitigation. Nevertheless, GIS applications continue to face challenges including inconsistent data quality, conflicts between model complexity and computational efficiency, and insufficient real-time capabilities. Moving forward, through integration with artificial intelligence, big data, and cloud computing, GIS will demonstrate greater potential in dynamic early warning systems, intelligent modelling, and interdisciplinary collaboration, delivering precise and efficient solutions for geological hazard prevention and control.
Keywords
Geographic Information System (GIS); Geological Hazard Assessment; Hazard Susceptibility Zoning; Multi-Source Data Integration
References
[1] Global Assessment Report on Disaster Risk Reduction. 2021. [2] Shao, Y., Zhang, M., & Xie, C. (2022). Current status and prospects of comprehensive remote sensing monitoring for geological hazards. Geology and Resources, 31(3), 381–394. [3] Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2020). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 203, 103134. [4] Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaei, M., & Moradi, H. R. (2023). Multi-hazard probability assessment and mapping in Iran. Science of The Total Environment, 857, 159439. [5] Lan, Y. Y., Guo, C. C., & Zhu, Y. F. (2024). Review of methods for geological hazard susceptibility assessment. Geology and Resources, 33(1), 65–73. [6] Liu, R. H., Li, M. H., Deng, Y. E., et al. (2021). GIS-based geological hazard susceptibility assessment in Huayingshi. Sedimentology and Tethys Geology, 41(1), 129–136. [7] Van Westen, C. J. (1997). GIS technology in mapping landslide hazard. In Geohazards in Engineering Geology. Springer, Dordrecht. [8] Chock, G., & Cardenas, C. (2014). Geological hazard and risk evaluation using GIS: San Jose Corridor, San Jose to Cabo San Lucas, Baja California Sur, Mexico. Environmental & Engineering Geoscience, 20(4), 359–376. [9] He, X., Li, Y., & Zhang, Y. (2024). Geological hazard risk assessment and rural settlement site selection using GIS and random forest algorithm. Ecological Indicators, 158, 111469. [10] Chen, J. (2019). Research and application of multi-level fuzzy comprehensive evaluation method in seismic hazard risk assessment. Chengdu University of Technology. [11] Cui, C. F. (2019). GIS- and AHP-based debris flow susceptibility assessment on both sides of the Taohe River Daoban–Tangwang section. Henan Polytechnic University.
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