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Research on Regional Classification of Mountain Flood Risk Based on Graph Neural Network Clustering in the Open-Pit Mining Area
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Zhen PAN1, 2, Haoxiang YANG3, Xiangpeng WANG4, Wei LI5, Donghao ZHAI6
Mining Research and Development | 2025, 45(12) : 144 - 152
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Mining Research and Development | 2025, 45(12): 144-152
Research on Regional Classification of Mountain Flood Risk Based on Graph Neural Network Clustering in the Open-Pit Mining Area
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Zhen PAN1, 2, Haoxiang YANG3, Xiangpeng WANG4, Wei LI5, Donghao ZHAI6
Affiliations
  • 1.Institute of Care-Life (ICL) of Chengdu High-Tech Zone, Chengdu, Sichuan 610095, China
  • 2.Multi-Hazard Early Warning Key Laboratory of Sichuan Province, Chengdu, Sichuan 611731, China
  • 3.Chengdu Meihuan Technology Co., Ltd, Chengdu, Sichuan 610041, China
  • 4.College of Geophysical, Chengdu University of Technology, Chengdu, Sichuan 610059, China
  • 5.145 Brigade Gansu Bureau of Coal Geology, Zhangye, Gansu 734000, China
  • 6.College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, Sichuan 610059, China
Published: 2025-12-25
Outline
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A method based on graph neural network (GNN) clustering was proposed for the regional classification of mountain flood risk in open-pit mining areas, so as to address the issue of spatial relationships neglected in existing classification methods. The research area selects the Baiyunebo mining area, and the classification method of mountain flood risk in mining area mainly consists of three steps. Firstly, the remote sensing images of the mining area were selected to divide into grid units and each grid was used as a node of the map. The node features mainly included elevation, slope, aspect, rainfall and precipitation. Secondly, the cosine similarity method was used to calculate the similarity of environmental features and represent the spatial relationship of similar nodes as edges, constructing an adjacency matrix. Thirdly, the constructed graph and corresponding adjacency matrix were input into the network model, which adopted an encoder architecture consisting of graph sample and aggregate (GraphSAGE) and graph attention network (GAT). The KL divergence loss function was used to optimize the model, ultimately achieving clustering prediction of the classification of mountain flood risk in the Bayyunebo mining area. The experimental results show that the clustering accuracy of the model reaches 87.5%, which is 11.2, 9.4 and 5.3 percentage points higher than traditional clustering algorithms k-means, hierarchical clustering, and random forest, respectively. The research result can verify the ability of this method to classify mountain flood risk in open-pit mining areas and can provide a reference for the prevention of mountain flood in open-pit mining areas.

Open-pit mining area  /  Classification of mountain flood risk  /  Cluster analysis  /  Graph neural network
潘臻, 杨浩翔, 王向鹏, 李伟, 翟东豪. 基于图神经网络聚类的露天矿区山洪风险等级区划研究. 矿业研究与开发, 2025 , 45 (12) : 144 -152 .
Zhen PAN, Haoxiang YANG, Xiangpeng WANG, Wei LI, Donghao ZHAI. Research on Regional Classification of Mountain Flood Risk Based on Graph Neural Network Clustering in the Open-Pit Mining Area[J]. Mining Research and Development, 2025 , 45 (12) : 144 -152 .
Year 2025 volume 45 Issue 12
47
22
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Article Info
  • Receive Date:2024-12-23
  • Online Date:2026-10-28
  • Published:2025-12-25
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History
  • Received:2024-12-23
Funding
Affiliations
    1.Institute of Care-Life (ICL) of Chengdu High-Tech Zone, Chengdu, Sichuan 610095, China
    2.Multi-Hazard Early Warning Key Laboratory of Sichuan Province, Chengdu, Sichuan 611731, China
    3.Chengdu Meihuan Technology Co., Ltd, Chengdu, Sichuan 610041, China
    4.College of Geophysical, Chengdu University of Technology, Chengdu, Sichuan 610059, China
    5.145 Brigade Gansu Bureau of Coal Geology, Zhangye, Gansu 734000, China
    6.College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, Sichuan 610059, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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