Building harmony between process-based and machine-learning models for hydrologic prediction
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Title: Building harmony between process-based and machine-learning models for hydrologic prediction
Abstract: Machine-learning (ML) based models are both popular and state-of-the-art in terms of predictive performance for hydrologic modeling. However, these models are often not very flexible once trained and are generally not usable beyond their targeted variables and spatiotemporal scales. On the other hand, process-based (PB) hydrologic models are flexible in their spatiotemporal resolutions and often simulate multiple processes together, making them extremely useful for scientific understanding and operational predictions across a wide range of target areas such as energy, agriculture, and flood forecasting. In this talk I will highlight my work in reconciling these approaches. I will discuss topics such as hybrid modeling, model emulation, differentiable hydrology, and how the ongoing explosion in data availability is changing the state of hydrologic predictions.