We present a knowledge\u2010guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. The forward model predicts streamflow using the learned catchment state. The FHNN architecture is designed to model multi\u2010scale processes and capture their interactions, a critical ability for flood modeling. FHNN also improves forecasts based on real\u2010time data through an inference\u2010based data integration approach using inverse modeling. FHNN’s data integration approach improves forecasts in response to observed data more efficiently than data assimilation methods that require computationally intensive optimization. We compare the FHNN to a leading deep learning alternative (autoregressive LSTM) on the large\u2010sample CAMELS\u2010US data set, and operational flood forecast data from the US National Weather Service (NWS). Official NWS flood forecasts are generated by expert human forecasters using a physics\u2010based model, in a human\u2010in\u2010the\u2010loop process. Thus, we assess the flood forecast ability of FHNN by directly comparing its performance against these NWS expert\u2010derived forecasts. The human forecaster creates a more accurate forecast within the first 12\u201318\u00a0hr of a forecast’s issuance, but FHNN has significantly better predictions thereafter. This research lays the groundwork for leveraging the predictive performance of AI\u2010based models with the expertise in forecasting agencies to produce better river forecasts.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a knowledge\u2010guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main components: inverse and forward models. The inverse model uses observed precipitation, temperature, and streamflow data to generate a representation of the current underlying catchment state. 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