Application Research on Geological Hazard Risk Zonation Evaluation Based on GIS Information Fusion:A Case Study of Bama County
DOI: https://doi.org/10.62517/jsse.202508403
Author(s)
Qiongzhen Tang1,*, Shenye Liu1,2, Jijun Zeng3
Affiliation(s)
1School of Intelligent Engineering, Xiangsihu College of Guangxi Minzu University, Nanning, Guangxi, China
2Guangxi Chuanjinnuo Chemical Co., Ltd., Fangchenggang, Guangxi, China
3Guangxi Hydrogeological and Engineering Geological Exploration Institute, Liuzhou, Guangxi, China
*Corresponding Author
Abstract
Given frequent landslide hazards and insufficient collaborative analysis of multi-source data in the carbonaceous mudstone mountainous areas of Bama County, this study uses GIS information fusion technology as the core to construct a three-level fusion framework. It is a "Data Layer-Feature Layer-Decision-making Layer" framework for geological hazard risk zonation evaluation. By integrating multi-source data including terrain (DEM), geology (structure, lithology), meteorology (rainfall), vegetation (NDVI) and historical hazards, the study area is divided into 5 risk levels, and the model accuracy is verified by 107 historical landslide points. The results show that: (1) GIS information fusion effectively integrates multi-source heterogeneous data, which solves the limitations of single data, and the model's recognition accuracy for high-risk areas reaches 89.7%; (2) the high-risk areas (Level 4-5) in Bama County account for 28.8%, concentrated in the area within < 0.5km around the northwestern Bama Fault Zone, the carbonaceous mudstone distribution area with slope > 25° and annual average rainfall > 1400mm; (3) the low-risk areas (Level 1-2) account for 46.6%, mainly distributed in the gentle areas with vegetation coverage > 60% in the southwest. Moreover, this study can serve as a reference for geological hazard prevention and control, as well as for risk management, in Bama County.
Keywords
GIS; Information Fusion; Geological Hazards; Risk Zonation; Carbonaceous Mudstone
References
[1]Huang Runqiu, Xu Qiang. Risk Assessment and Management of Geological Hazards in China. Beijing: Science Press, 2020: 124-136.
[2]Ugliotti M F ,Osello A ,Daud M , et al.Enhancing Risk Analysis toward a Landscape Digital Twin Framework: A Multi-Hazard Approach in the Context of a Socio-Economic Perspective. Sustainability, 2023, 15(16): DOI: 10.3390/SU151612429.
[3]Shiono M, Takahashi T. GIS-based landslide susceptibility mapping using weighted linear combination method in the Kii Peninsula, Japan. Landslides, 2005, 2(3): 213-222.
[4]Wang Tao, Li Weile, Zhang Guirong. Experimental study on mechanical properties of carbonaceous mudstone in Bama County. Journal of Engineering Geology, 2022, 30(4): 1123-1130.
[5]Zhang Guirong, Wang Tao, Li Weile. Landslide susceptibility assessment of carbonaceous mudstone mountainous areas in Guizhou based on random forest. Geography and Geo-Information Science, 2023, 39(2): 90-96.
[6]Rivera A C ,Reina V V S ,Naranjo G F L , et al.Geospatial Landslide Risk Mapping Using AHP and GIS: A Case Study of the Utcubamba River Basin, Peru. Applied Sciences, 2025, 15(17):9423-9423.DOI:10.3390/APP15179423.
[7]Demirel B ,Yildirim E ,Can E .GIS-based landslide susceptibility mapping using AHP, FMEA, and Pareto systematic analysis in central Yalova, Türkiye .Engineering Science and Technology, an International Journal,2025,64102013-102013.DOI: 10.1016/J.JESTCH.2025.102013.
[8]Chaabane Z F ,Lamine S ,Guettouche S M , et al.Landslide Risk Assessments through Multicriteria Analysis .ISPRS International Journal of Geo-Information,2024,13(9):303-303.DOI: 10.3390/IJGI13090303.
[9]Enkela B ,Albana K ,Aida H .Landslide Risk Assessment of Settlements Using GIS and Remote Sensing in Kashar, Albania .Journal of Geography, Environment and Earth Science International,2025,29(7):191-202.DOI: 10.9734/JGEESI/2025/V29I7926.
[10]Ambika K ,Alzaben N ,Alghamdi G A , et al.Integrated geotechnical and remote sensing-based monitoring of unstable slopes for landslide early warning using IoT and sensor networks .Journal of South American Earth Sciences,2025,164105666-105666.DOI: 10.1016/J.JSAMES.2025.105666.