Author
Listed:
- Pingping Luo
(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang’an University, Ministry of Education, Xi’an 710054, China
Shaanxi Province Innovation and Introduction Base for Discipline of Urban and Rural Water Security and Rural Revitalization in Arid Areas, Chang’an University, Xi’an 710054, China
School of Water and Environment, Chang’an University, Xi’an 710054, China
Xi’an Monitoring, Modeling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang’an University, Xi’an 710054, China)
- Hanming Zhang
(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang’an University, Ministry of Education, Xi’an 710054, China
Shaanxi Province Innovation and Introduction Base for Discipline of Urban and Rural Water Security and Rural Revitalization in Arid Areas, Chang’an University, Xi’an 710054, China
School of Water and Environment, Chang’an University, Xi’an 710054, China
Xi’an Monitoring, Modeling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang’an University, Xi’an 710054, China)
- Chen Su
(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang’an University, Ministry of Education, Xi’an 710054, China
Shaanxi Province Innovation and Introduction Base for Discipline of Urban and Rural Water Security and Rural Revitalization in Arid Areas, Chang’an University, Xi’an 710054, China
School of Water and Environment, Chang’an University, Xi’an 710054, China
Xi’an Monitoring, Modeling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang’an University, Xi’an 710054, China)
- Jiaxin Zhong
(Ningxia Institute of Survey and Monitoring of Land and Resources, Yinchuan 750002, China
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China)
- Fatima Fida
(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang’an University, Ministry of Education, Xi’an 710054, China
Shaanxi Province Innovation and Introduction Base for Discipline of Urban and Rural Water Security and Rural Revitalization in Arid Areas, Chang’an University, Xi’an 710054, China)
- Weili Duan
(State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China)
- Mohd Remy Rozainy Mohd Arif Zainol
(River Engineering and Urban Drainage Research Center (REDAC), Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia)
- Qiaomin Li
(Remote Sensing Investigation Institute of Ningxia Hui Autonomous Region, Yinchuan 750021, China)
- Wei Zhu
(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Chang’an University, Ministry of Education, Xi’an 710054, China
Shaanxi Province Innovation and Introduction Base for Discipline of Urban and Rural Water Security and Rural Revitalization in Arid Areas, Chang’an University, Xi’an 710054, China
School of Water and Environment, Chang’an University, Xi’an 710054, China
Xi’an Monitoring, Modeling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang’an University, Xi’an 710054, China)
- Chong-yu Xu
(Department of Geosciences, University of Oslo, 0315 Oslo, Norway)
Abstract
The escalating consequences of human activities and global warming have markedly increased the frequency and intensity of geological disasters worldwide, posing a formidable threat to human life and property. In the southern mountainous region of Ningxia, China—an area characterized by complex topography, interlaced ravines, and pronounced ecological fragility—recurrent geological disasters have substantially constrained rural revitalization and development. This study introduces the integration of the Information Value (IV) method with Random Forest (RF) and XGBoost models, identifying IV + XGBoost as the optimal model through rigorous ROC-curve validation. The results reveal that low- and lower-risk areas account for 58.63% of the total area (7644.20 km 2 and 4038.08 km 2 ), medium-risk areas cover 29.24% (5825.76 km 2 ), and high-risk regions constitute 12.13% (2417.28 km 2 ). The latter are predominantly in river valleys with high population density and intensive economic activities. These findings provide practical recommendations for scientifically informed disaster management and decision-making by relevant authorities. Furthermore, the proposed methodology offers valuable insights for disaster risk assessment in other regions with similar complex terrains and ecological vulnerabilities, contributing to developing more effective and sustainable disaster mitigation strategies.
Suggested Citation
Pingping Luo & Hanming Zhang & Chen Su & Jiaxin Zhong & Fatima Fida & Weili Duan & Mohd Remy Rozainy Mohd Arif Zainol & Qiaomin Li & Wei Zhu & Chong-yu Xu, 2025.
"Geological Disaster Susceptibility and Risk Assessment in Complex Mountainous Terrain: A Case Study from Southern Ningxia, China,"
Land, MDPI, vol. 14(10), pages 1-27, September.
Handle:
RePEc:gam:jlands:v:14:y:2025:i:10:p:1961-:d:1760406
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