Physics-Guided Meta-Learning Method in Baseflow Prediction over Large Regions
Shengyu Chen, Yiqun Xie, Xiang Li, Liang Xu, Xiaowei Jia
Abstract
Physics-based groundwater flow equations are powerful tools for water resource assessment under different hydrological and climatic conditions. How these conditions affect the discharge of groundwater (i.e., base-flow) into rivers is one of the most important topics in the hydrology domain. However, due to the different environmental conditions in different basins, it is difficult to use a single physics-based equation to represent the discharge of groundwater in all river basins. Despite the promise of data-driven models in capturing complex relationships, they are also limited in learning heterogeneous baseflow patterns from multiple basins, especially with sparse training data. In this paper, we propose a new data-driven model Physics Guided MeTa Learning (PGMTL), which uses meta-learning to adapt the predictive model to multiple basins and also enhance the meta-learning process with knowledge embodied in different physics-based equations so as to improve the baseflow prediction over a large number of river basins. Experimental results show that our proposed PGMTL has a significant improvement over either physics-based equations or ML models. Moreover, our method has been shown to perform much better with sparse or localized training data. Finally, our method is able to interpret the contribution of each physics-based equation under different scenarios.