Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province
This study focuses on Huangshi Town, Longchuan County, Guangdong Province, as the research area. By utilizing existing data and field surveys, the study identifies geological hazards and risks in the area, deeply analyzes the formation mechanisms and disaster-causing patterns, and systematically sum...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5298 |
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| author | Gonghao Duan Hui Xia Anqi Du Juan Ma |
| author_facet | Gonghao Duan Hui Xia Anqi Du Juan Ma |
| author_sort | Gonghao Duan |
| collection | DOAJ |
| description | This study focuses on Huangshi Town, Longchuan County, Guangdong Province, as the research area. By utilizing existing data and field surveys, the study identifies geological hazards and risks in the area, deeply analyzes the formation mechanisms and disaster-causing patterns, and systematically summarizes the developmental characteristics and spatial distribution of these hazards. Methodologically, the research combines regular grids and slope units, selecting ten evaluation factors for correlation analysis. The information theory model is used to assess disaster susceptibility. The study further evaluates the impact of three rainfall scenarios—100 mm of rainfall over 24 h, 250 mm of rainfall over 24 h, and 240 mm of effective rainfall over 72 h—on geological disasters in Huangshi Town. As a result, a comprehensive hazard assessment under multiple rainfall scenarios is produced. The findings show that the Receiver Operating Characteristic (ROC) accuracy of the disaster-susceptibility evaluation reaches 0.8739, ensuring high data quality for the geological hazard assessment in Huangshi Town. The zoning results align closely with field survey observations. In conclusion, incorporating rainfall as a triggering factor enhances the accuracy of the susceptibility analysis by better capturing the temporal and spatial patterns of landslide occurrences, thereby offering a more comprehensive understanding of the geological hazard development in the study area. |
| format | Article |
| id | doaj-art-5a45d426b4e14e28a81f03364389e57c |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5a45d426b4e14e28a81f03364389e57c2025-08-20T03:14:45ZengMDPI AGApplied Sciences2076-34172025-05-011510529810.3390/app15105298Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong ProvinceGonghao Duan0Hui Xia1Anqi Du2Juan Ma3School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, ChinaTechnology Innovation Center for Geo-Hazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, ChinaThis study focuses on Huangshi Town, Longchuan County, Guangdong Province, as the research area. By utilizing existing data and field surveys, the study identifies geological hazards and risks in the area, deeply analyzes the formation mechanisms and disaster-causing patterns, and systematically summarizes the developmental characteristics and spatial distribution of these hazards. Methodologically, the research combines regular grids and slope units, selecting ten evaluation factors for correlation analysis. The information theory model is used to assess disaster susceptibility. The study further evaluates the impact of three rainfall scenarios—100 mm of rainfall over 24 h, 250 mm of rainfall over 24 h, and 240 mm of effective rainfall over 72 h—on geological disasters in Huangshi Town. As a result, a comprehensive hazard assessment under multiple rainfall scenarios is produced. The findings show that the Receiver Operating Characteristic (ROC) accuracy of the disaster-susceptibility evaluation reaches 0.8739, ensuring high data quality for the geological hazard assessment in Huangshi Town. The zoning results align closely with field survey observations. In conclusion, incorporating rainfall as a triggering factor enhances the accuracy of the susceptibility analysis by better capturing the temporal and spatial patterns of landslide occurrences, thereby offering a more comprehensive understanding of the geological hazard development in the study area.https://www.mdpi.com/2076-3417/15/10/5298landslide predictionrisk assessmentrainfall conditionsinformation theoretic modelinducing factors |
| spellingShingle | Gonghao Duan Hui Xia Anqi Du Juan Ma Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province Applied Sciences landslide prediction risk assessment rainfall conditions information theoretic model inducing factors |
| title | Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province |
| title_full | Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province |
| title_fullStr | Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province |
| title_full_unstemmed | Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province |
| title_short | Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong Province |
| title_sort | risk assessment of geological hazards based on multi condition development scenarios a case study of huangshi town guangdong province |
| topic | landslide prediction risk assessment rainfall conditions information theoretic model inducing factors |
| url | https://www.mdpi.com/2076-3417/15/10/5298 |
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