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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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84825-y |
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| author | Weifeng Xiao Ziyuan Zhou Bozhi Ren Xinping Deng |
| author_facet | Weifeng Xiao Ziyuan Zhou Bozhi Ren Xinping Deng |
| author_sort | Weifeng Xiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-39943c7acc41436aa7b7bcc2787251e9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-39943c7acc41436aa7b7bcc2787251e92025-01-19T12:17:25ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84825-yIntegrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan ProvinceWeifeng Xiao0Ziyuan Zhou1Bozhi Ren2Xinping Deng3School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and TechnologySchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and TechnologySchool of Earth Sciences and Spatial Information Engineering, Hunan University of Science and TechnologyHunan Geological Disaster Monitoring Early Warning and Emergency Rescue Engineering Technology Research CenterAbstract 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.https://doi.org/10.1038/s41598-024-84825-yGeological hazardsSelf-organizing map (SOM)Composite geological hazard indexLinear mixed models (LMMs)Spatial heterogeneity |
| spellingShingle | Weifeng Xiao Ziyuan Zhou Bozhi Ren Xinping Deng Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province Scientific Reports Geological hazards Self-organizing map (SOM) Composite geological hazard index Linear mixed models (LMMs) Spatial heterogeneity |
| title | Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province |
| title_full | Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province |
| title_fullStr | Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province |
| title_full_unstemmed | Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province |
| title_short | Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province |
| title_sort | integrating spatial clustering and multi source geospatial data for comprehensive geological hazard modeling in hunan province |
| topic | Geological hazards Self-organizing map (SOM) Composite geological hazard index Linear mixed models (LMMs) Spatial heterogeneity |
| url | https://doi.org/10.1038/s41598-024-84825-y |
| work_keys_str_mv | AT weifengxiao integratingspatialclusteringandmultisourcegeospatialdataforcomprehensivegeologicalhazardmodelinginhunanprovince AT ziyuanzhou integratingspatialclusteringandmultisourcegeospatialdataforcomprehensivegeologicalhazardmodelinginhunanprovince AT bozhiren integratingspatialclusteringandmultisourcegeospatialdataforcomprehensivegeologicalhazardmodelinginhunanprovince AT xinpingdeng integratingspatialclusteringandmultisourcegeospatialdataforcomprehensivegeologicalhazardmodelinginhunanprovince |