FDRL:一种基于数据的算法,用于使用常规和传统的土壤特征预测喜马拉雅山脉的沉降速度
Sahil Sankhyan1, Ajoy Kumar1, Praveen Kumar1
1ACS Lab, Indian Institute of Technology Mandi, Kamand, India.
Scientific reports
|August 13, 2025
概括
这项研究引入了一种新的数据驱动模型,用于预测喜马拉雅山脉的山体滑坡沉降速度. 它将传统的土壤知识与地质技术数据相结合,使滑坡风险管理更加准确和可解释.
科学领域:
- 地质技术工程 地质技术工程
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 在喜马拉雅山脉,山体滑坡带来了重大风险,影响了人们的生活和基础设施.
- 现有的沉降速度模型经常使用孤立的数据源,限制了准确性.
- 需要采用综合方法,将各种数据结合起来,以便更好地预测山体滑坡.
研究的目的:
- 开发一个可解释的数据驱动模型,用于增强滑坡沉降速度预测.
- 将传统的土壤信息与地质技术特征相结合,以提高预测能力.
- 将当地知识系统与现代数据科学相结合,用于滑坡风险管理.
主要方法:
- 开发了一个叫做预测数据驱动回归学习 (FDRL) 的堆叠集团回归模型.
- 采用机器学习技术,包括特征选择 (皮尔森相关性,相互信息分数).
- 结合了定量地质技术变量与定性传统土壤指标.
主要成果:
- 在FDRL模型中,训练RMSE达到1.11mm/年,测试RMSE达到1.32mm/年.
- 可解释性分析 (SHAP) 证实了地质技术和传统土壤特征的重大贡献.
- 证明了将定性,本地来源的土壤数据整合到科学模型中的实用性.
结论:
- 混合方法显著提高了滑坡沉降速度预测的准确性.
- 对于早期预警系统,FDRL模型提供了一个可扩展,可解释和可在本地实施的解决方案.
- 整合传统的土壤指标可以提高山体滑坡风险管理策略的实际适用性.
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