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基于斜坡单位和机器学习方法的温州滑坡危险风险研究
Dengjie Kang1, Sheng Dan2, Zhang Hua3
1School of National Safety and Emergency Management, Beijing Normal University, Beijing, 100875, China.
Scientific reports
|March 3, 2025
概括
一种基于斜率单位的新方法提高了山体滑坡危险评估的准确性. 机器学习模型,特别是随机森林,提高了预测可靠性,这对于脆弱地区的有效灾害管理至关重要.
科学领域:
- 地质地质地质地质地质地
- 地质技术工程 地质技术工程
- 环境科学 环境科学
背景情况:
- 滑坡带来了重大的地质灾害风险,传统的评估方法缺乏特定地点的细节性加剧了这种风险.
- 目前的行政或基于电网的山体滑坡风险评估导致山区的高估,增加管理复杂性和成本.
研究的目的:
- 开发和验证一种基于斜率单位的新型滑坡危险评估方法.
- 为了比较机器学习模型 (随机森林,XGBoost,LightGBM) 的性能,进行滑坡易感性分析.
- 评估极端降雨事件对山体滑坡风险的影响.
主要方法:
- 利用历史的山体滑坡数据和机器学习算法 (随机森林,XGBoost,LightGBM) 来进行易感性分析.
- 在温州市实施基于斜率单位的方法进行细粒度危险评估.
- 分析了降雨回归周期对山体滑坡风险比例的影响.
主要成果:
- 滑坡易感性被分为六个级别,其中非常高风险地区占总数的5.35%.
- 随机森林模型在测试的机器学习模型中实现了最高的预测准确率 (77.94%).
- 极端降雨 (100年回归期) 将非常高风险的斜坡单位增加到40.39%,突出显示了降雨的关键作用.
结论:
- 基于斜率单位的方法提供了一个比传统方法更精确的方法来评估山体滑坡危险.
- 机器学习模型,特别是随机森林,在斜率单位层面提供可靠的滑坡易感性预测.
- 调查结果强调了需要动态风险评估,考虑到降雨事件和对高风险区域实施有针对性的预防措施.
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