使用支持性预测模型对土地沉降率的审查:结合雷达干扰测量和整体软计算
Bahram Choubin1, Kourosh Shirani2, Farzaneh Sajedi Hosseini3
1Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran.
Journal of environmental management
|July 30, 2023
概括
这项研究整合了雷达数据和机器学习来模拟土地沉降率,识别了降水和地下水撤退等关键驱动因素. 整体模型,特别是非线性方法,在预测沉降方面表现出卓越的表现.
科学领域:
- 地质科学和遥感技术
- 环境科学 环境科学
- 水资源管理 水资源管理
背景情况:
- 土地沉降对土地和水资源管理构成重大挑战.
- 雷达数据集提供了一种具有成本效益的方法来监测沉降.
- 识别关键驱动因素对于准确的沉降建模至关重要.
研究的目的:
- 开发一种基于雷达和总体的综合方法来模拟土地沉降率.
- 通过机器学习识别影响土地沉降的主要驱动因素.
- 为了比较单个和整体机器学习模型的性能,用于沉降预测.
主要方法:
- 利用了52对2014-2019年的雷达图像来检测沉降.
- 采用模拟回火 (SA) 算法来识别关键沉降驱动因素.
- 对比了三种单独的机器学习模型 (SVM,GP,BART) 和三种集体方法进行沉降建模.
主要成果:
- 在2014年至2019年期间,沉降率从0到59厘米不等.
- 雷达衍生沉降显示与地力学站数据有很强的相关性 (R2 = 0.99).
- 确定的主要驱动因素包括降水,海拔,细粒度材料,地下水的提取,距离道路,地下水的下降,以及含水层的厚度.
- 整体模型的表现优于单个模型,非线性整体 (BART组合) 的表现最好 (RMSE = 0.061,R2 = 0.83).
结论:
- 非线性整体方法有效地模拟了土地沉降率.
- 含水层系统中的细粒度材料对地下水变化的沉降反应有显著的影响.
- 地下水减少,充电不足和细粒度材料的相互作用加剧了土地沉降,造成了相当大的风险.
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