Abstract
While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation.
Original language | English (US) |
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Article number | 3633 |
Journal | Water (Switzerland) |
Volume | 13 |
Issue number | 24 |
DOIs | |
State | Published - Dec 1 2021 |
All Science Journal Classification (ASJC) codes
- Biochemistry
- Geography, Planning and Development
- Aquatic Science
- Water Science and Technology
Keywords
- Hydrologic modeling
- Hydrologic runoff processes
- Machine learning
- Model emulation
- Simulation-based inference