Knowledge-Guided Machine Learning for Operational Flood Forecasting

Recent advances in Machine Learning (ML) have made strides in improving the prediction of river levels, and often outperform physics-based models that make a forecast based on the physics of how rivers respond to precipitation. However, expert human forecasters with field intelligence know how to modify and improve physics-based models to achieve high accuracy. Our research responds to key needs in the operational forecasting community: (a) by making direct comparisons between ML models and official forecasts that are based on both physics-based models and forecaster expertise, and (b) by combining the strengths of pure physics-based and ML approaches. We have developed a new “Knowledge-Guided” ML (KGML) model with its algorithm informed directly by hydrologic science, called the Factorized Hierarchical Neural Network, and demonstrate that it performs as well or better than NWS flood forecasts 2–7 days after a forecast is issued, and better than a leading ML alternative that does not incorporate physical science knowledge in its architecture. An expert human forecaster using a physics-based model is still more skilled than the state-of-the-art ML methods within the first day. If forecasters could use our KGML approach operationally, the skill of river forecasts has the potential to improve substantially.