基于机器学习的关键铁路沿线地质危险易感性的评估
Jiarong Liang1,2, Wenwen Qi3,4, Chong Xu1,5
1National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, 100085, China.
Scientific reports
|November 27, 2025
概括
这项研究使用随机森林 (RF) 建模绘制了合肥 - 福州高速铁路的山体滑坡易感性. 高风险区域与坡和大雨相关,指导铁路基础设施安全.
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
- 遥感 遥感 遥感 遥感
背景情况:
- 像山体滑坡这样的地质危险威胁着铁路基础设施.
- 高铁是重要的交通纽带,特别是在地质复杂的地区.
- 准确地绘制山体滑坡易感性的地图对于基础设施安全和防灾至关重要.
研究的目的:
- 评估合肥 - 福州高速铁路在一个关键段的山体滑坡易感性.
- 开发和验证滑坡发生的预测模型.
- 确定导致该地区滑坡易发生的关键因素.
主要方法:
- 利用历史的山体滑坡库存进行模型训练.
- 采用随机森林 (RF) 算法进行易感性建模.
- 分析了高风险区域与环境因素 (如斜率和降水) 之间的空间相关性.
主要成果:
- 随机森林模型展示了出色的预测性能.
- 研究区域的很大一部分被确定为高和极高的山体滑坡易感区域.
- 发现坡和高年降水量与高风险地区有很强的相关性.
结论:
- 开发的山体滑坡易感性地图为铁路沿线防灾提供了关键指导.
- 斜率被确定为主要影响因素,其他因素在区域上有差异.
- 该研究为类似的发展中地区的基础设施规划和地质风险管理提供了有价值的框架.
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