论文标题
将参数地面模型与机器学习结合
Combining Parametric Land Surface Models with Machine Learning
论文作者
论文摘要
在少数Ameriflux站点提出并评估了混合机器学习和基于过程的建模方法（PBM）方法，以模拟顶层的土壤水分状态。此处采用的Hybrid-PBM（HPBM）使用与高斯工艺集成的Noah Landface模型。它旨在仅在与训练数据相似的气候情况下纠正模型，其恢复为PBM。这样，我们的方法避免了在无法获得类似培训数据并结合我们对系统的物理理解的情况下的情况下的错误预测。在这里，我们假设一个自回旋模型，并在每个选定的站点上使用一年的一年左右交叉验证，以超过3倍的RMSE降低了样本外结果。概述了使用混合建模来构建全球土地表面模型的路径，从而有可能显着胜过当前最新的。
A hybrid machine learning and process-based-modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state. The Hybrid-PBM (HPBM) employed here uses the Noah land-surface model integrated with Gaussian Processes. It is designed to correct the model only in climatological situations similar to the training data else it reverts to the PBM. In this way, our approach avoids bad predictions in scenarios where similar training data is not available and incorporates our physical understanding of the system. Here we assume an autoregressive model and obtain out-of-sample results with upwards of a 3-fold reduction in the RMSE using a one-year leave-one-out cross-validation at each of the selected sites. A path is outlined for using hybrid modeling to build global land-surface models with the potential to significantly outperform the current state-of-the-art.
