Embedding neural networks into physics-based hydrologic models
Abstract: Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process-based hydrologic models (PBHM). While DL methods have been able to achieve superior predictive performance for specific tasks, the ability of PBHMs to simulate the entire hydrologic cycle makes them useful for a wide range of modeling and simulation tasks. We take advantage of both of these approaches by coupling a DL model into a PBHM as sub-components for the simulation of evaporation and heat transfer. In this seminar we will describe the workflow and technologies needed to perform this coupling, as well as provide an outlook for the future of such applications. Our results demonstrate that the DL parameterizations can outperform physics-based equations for evaporation and heat transfer in several ways. We show that the DL parameterizations show improvements in predictive performance as well as provide more realistic simulations of other aspects which were not directly trained for by taking advantage of information from other model components. We then explore how the neural networks were able to more accurately simulate evaporation and heat transfer by analyzing the network with a method known as layerwise relevance propagation (LRP). We show how the neural networks were able to learn physically realistic relationships between input and output, as well as learning general site characteristics which were not included in the training data. Our work demonstrates how combining modeling approaches can lead to better predictions and hints at how such methods may allow us to derive better scientific understanding directly from large datasets
Andrew Bennett is a research scientist in the Computational Hydrology research group at the University of Washington. He received his PhD in civil and environmental engineering from the University of Washington in 2021 and was previously a research assistant in the Computer Science and Mathematics Division at Oak Ridge National Laboratory. Andrew's research focuses on hydrologic model development and using machine learning to improve our understanding of hydrologic systems.