Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province
Abstract This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-S...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84825-y |
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| Summary: | Abstract This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province. Importantly, the relationships between these key factors and GI exhibit spatial variability, as evidenced by the random intercept and slope models, highlighting the need for customized mitigation strategies. Additionally, the study demonstrates that land use patterns and stratigraphic stratum lithology significantly impact the cluster-specific relationships between the key factors and GI, emphasizing the importance of natural resource management for effective geological hazard mitigation. The proposed integrated framework provides valuable insights for policymakers and resource managers to develop spatially-aware strategies for geological hazard risk reduction and climate change adaptation. |
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| ISSN: | 2045-2322 |