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整合空间聚类与多源地理空间数据用于湖南省综合地质灾害建模

Integrating spatial clustering and multi-source geospatial data for comprehensive geological hazard modeling in Hunan Province.

作者信息

Xiao Weifeng, Zhou Ziyuan, Ren Bozhi, Deng Xinping

机构信息

School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China.

Hunan Geological Disaster Monitoring Early Warning and Emergency Rescue Engineering Technology Research Center, Changsha, 410004, China.

出版信息

Sci Rep. 2025 Jan 15;15(1):1982. doi: 10.1038/s41598-024-84825-y.

DOI:10.1038/s41598-024-84825-y
PMID:39809817
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11732972/
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.

摘要

本研究提出了一个综合框架,该框架结合了空间聚类技术和多源地理空间数据,以全面评估和了解中国湖南省的地质灾害。该研究整合了自组织映射(SOM)和地理自组织映射(Geo-SOM),以探索环境因素与各种地质灾害(包括滑坡、边坡失稳、崩塌、地面沉降和泥石流)发生之间的关系。关键研究结果表明,年平均降水量(Pre)、剖面曲率(Pro_cur)和坡度(Slo)是影响全省综合地质灾害指数(GI)的主要因素。重要的是,这些关键因素与GI之间的关系呈现出空间变异性,随机截距和斜率模型证明了这一点,突出了制定定制化减灾策略的必要性。此外,研究表明土地利用模式和地层岩性显著影响关键因素与GI之间特定聚类的关系,强调了自然资源管理对有效减轻地质灾害的重要性。所提出的综合框架为政策制定者和资源管理者制定具有空间意识的地质灾害风险降低和气候变化适应策略提供了有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/162393a3e91d/41598_2024_84825_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/7f2136a1b812/41598_2024_84825_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/4122ed861848/41598_2024_84825_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/c324b65732cb/41598_2024_84825_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/162393a3e91d/41598_2024_84825_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/7f2136a1b812/41598_2024_84825_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/65e622c11a95/41598_2024_84825_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/12087d4f22b5/41598_2024_84825_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/4122ed861848/41598_2024_84825_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/c324b65732cb/41598_2024_84825_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/a87a2702f7da/41598_2024_84825_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/994f/11732972/162393a3e91d/41598_2024_84825_Fig7_HTML.jpg

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