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从地物理学的半自动湿度估计的实地评估,使用机器学习
Neil Terry1, Frederick D Day-Lewis2, John W Lane3
1U.S. Geological Survey, New York Water Science Center, 126 Cooke Hall, University at Buffalo North Campus, Buffalo, New York, USA.
Vadose zone journal : VZJ
|September 13, 2023
概括
地质物理方法可以估计土壤水分,但结合地面透雷达,电电阻断层扫描和频域电磁学的数据可以提高准确性. 机器学习模型增强了土壤湿度预测,用于更好的特定场所应用.
科学领域:
- 地质物理学 地质物理学
- 土壤科学 土壤科学
- 机器学习 机器学习
背景情况:
- 地质物理方法提供3D土壤湿度估计,但与测量的直接比较往往很差,将其使用限制在定性评估.
- 局限性包括需要准确的模型,来自数据处理的不确定性,以及整合来自多种地球物理技术的数据的挑战.
- 准确的土壤湿度估计对于各种环境和农业应用至关重要.
研究的目的:
- 调查地质物理方法的局限性,用于定量地土湿度估计.
- 评估结合多个地球物理数据集 (GPR,ERT,FDEM) 进行土壤湿度预测的有效性.
- 探索机器学习的应用,以改善土壤湿度建模.
主要方法:
- 进行了一项灌实验,监测土壤水分,并在灌前后收集表面地质物理数据 (GPR,ERT,FDEM).
- 地理学数据被处理,格式化,并使用校准技术,多变量回归和机器学习来预测土壤水分.
- 采用随机回归森林模型,使用反向ERT,原始FDEM和反向FDEM数据的组合.
主要成果:
- 一个机器学习模型结合了反向ERT,原始FDEM和反向FDEM数据,在预测土壤水分方面取得了很高的准确性 (RMSE 0.025-0.046 cm3/cm3).
- 该模型的性能通过交叉验证和单独的测试数据集得到验证,证实了其稳定性.
- 机器学习为模型选择提供了一个半自动化过程,可以适应不同的网站和数据集.
结论:
- 将多个地球物理数据集与机器学习相结合,可以显著提高定量土壤湿度估计的准确性.
- 开发的方法克服了个别地质物理方法的局限性和数据整合的挑战.
- 这种方法为开发针对不同应用的局部,准确的土壤湿度预测模型提供了一条途径.
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