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使用随机森林模型对邻近的美国进行水表深度估计
Yueling Ma1,2, Elena Leonarduzzi1,2, Amy Defnet1,2,3
1High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA.
Ground water
|October 5, 2023
概括
一个新的数据驱动的随机森林模型准确地估计了整个连续的美国 (CONUS) 的水表深度 (WTD). 这种方法为大规模淡水资源评估提供了传统模型的可靠替代方案.
科学领域:
- 水文学和水文地质学
- 地理空间数据科学数据科学
- 环境建模环境建模
背景情况:
- 水平面深度 (WTD) 显著影响地下水动态和陆地表面相互作用.
- 有限的WTD观测对准确的大规模地下水建模提出了挑战.
- 现有的基于物理的模型很难准确地代表观察到的WTD.
研究的目的:
- 开发和验证一种纯粹基于数据的方法,以估计大陆范围内的WTD.
- 评估随机森林 (RF) 模型在整个连续美国 (CONUS) 的WTD估计的性能.
- 量化与射频模型的WTD预测相关的不确定性.
主要方法:
- 应用了一个随机森林 (RF) 模型,使用可用的WTD观测数据来对CONUS进行估计.
- 利用定量回归森林来量化预测不确定性.
- 使用Pearson相关系数 (r),Nash-Sutcliffe效率 (NSE) 和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 射频模型与井观测结果达成高度一致 (r=0.96,NSE=0.93).
- 位置被确定为WTD估计的最关键特征,可能充当空间替代品.
- 高度的预测不确定性通常在浅水WTD地区被发现.
结论:
- 数据驱动的射频模型为大规模WTD估计提供了基于物理的方法的强大而准确的替代方案.
- 该模型为大陆范围的淡水资源提供了有价值的见解.
- 经过训练的射频模型显示了可转移到具有类似水文制度和有限数据的其他地区的潜力.
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