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Disaster-Pregnant Environment Stability Evaluation of Geohazards in the Yellow River–Huangshui River Valley, China

Author

Listed:
  • Tengyue Zhang

    (School of Geography, Qinghai Normal University, Xining 810008, China)

  • Qiang Zhou

    (School of Geography, Qinghai Normal University, Xining 810008, China
    Academy of Plateau Science and Sustainability, Xining 810008, China
    School of National Safety and Emergency Management, Qinghai Normal University, Xining 810008, China)

  • Weidong Ma

    (School of Geography, Qinghai Normal University, Xining 810008, China
    Academy of Plateau Science and Sustainability, Xining 810008, China
    School of National Safety and Emergency Management, Qinghai Normal University, Xining 810008, China)

  • Yuan Gao

    (School of Geography, Qinghai Normal University, Xining 810008, China)

  • Hanmei Li

    (School of Geography, Qinghai Normal University, Xining 810008, China)

  • Qiuyang Zhang

    (School of Geography, Qinghai Normal University, Xining 810008, China)

Abstract

This study aims to identify the key factors contributing to the destabilization of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley and provide a robust scientific basis for proactive disaster prevention, management of disaster chains, and mitigation of multi-hazard clusters in unstable regions. The research focuses on the Yellow River–Huangshui River Valley, evaluating the stability of its geohazard disaster-pregnant environment. The disaster-pregnant environment is classified into static and dynamic categories. The static disaster-pregnant environment encompasses factors such as lithology, fracture density, topography, slope, river network density, and vegetation cover. The dynamic disaster-pregnant environment incorporates variables such as extreme rainfall, consecutive rainy days, annual rainfall averages, monthly high temperatures, monthly maximum temperature variations, average annual air temperatures, and human activities. A random forest model was employed to quantitatively assess the stability of the geohazard disaster-pregnant environment in the Yellow River–Huangshui River Valley. The findings indicated that (1) extreme indicators were the primary contributors to the destabilization of the disaster-pregnant environment, with very heavy rainfall contributing 28% and consecutive rainy days contributing 27%. Human activities ranked next, accounting for 15%. (2) Unstable regions for static, dynamic, and integrated disaster-pregnant environments accounted for 44%, 45%, and 44% of the study area, respectively, with all unstable areas concentrated in river valley regions. (3) The overall trend of stability in the disaster-pregnant environment was characterized by widespread instability. Extremely unstable areas were predominantly located in river valley regions, largely influenced by human activities. Conversely, only 0.1% of the region exhibited signs of stability, and 2.1% showed a tendency toward extreme stability.

Suggested Citation

  • Tengyue Zhang & Qiang Zhou & Weidong Ma & Yuan Gao & Hanmei Li & Qiuyang Zhang, 2025. "Disaster-Pregnant Environment Stability Evaluation of Geohazards in the Yellow River–Huangshui River Valley, China," Sustainability, MDPI, vol. 17(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:732-:d:1569761
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    References listed on IDEAS

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    1. Zuo Sun & Yingjie Liu & Qingjie Qi & Wengang Liu & Dan Li & Jiamei Chai, 2022. "Risk Assessment of Coal Mine Flood Disasters Based on Projection Pursuit Clustering Model," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    2. Xin Zhang & Lijun Jiang & Wei Deng & Zhile Shu & Meiben Gao & Guichuan Liu, 2024. "Risk Assessment of Geological Hazards in the Alpine Gorge Region and Its Influencing Factors: A Case Study of Jiulong County, China," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
    3. Xiujie Wang & Zihua Qu & Fuchang Tian & Yanpeng Wang & Ximin Yuan & Kui Xu, 2023. "Ice-jam flood hazard risk assessment under simulated levee breaches using the random forest algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 331-355, January.
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