Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes

Current modeling approaches in hydrology often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to have rigid structures resulting in unrealistic parameter values in certain instances, ML algorithms establish the input–output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics-based model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may be deceptive. Hence, there is a need for a hybrid modeling approach to couple ML algorithms and physics-based models in a synergistic manner. Here we develop the Physics Informed Machine Learning (PIML) model that combines the process understanding of conceptual hydrological model with predictive abilities of state-of-the-art ML models. We apply the proposed model to predict the monthly time series of the target (streamflow) and intermediate variables (actual evapotranspiration) in ten different subcatchments in peninsular India. Our results show the capability of the PIML model to outperform a purely conceptual model (abcd model) and ML algorithms while ensuring the physical consistency in outputs validated through water balance analysis. The systematic approach for combining conceptual model structure with ML algorithms could be used to improve the predictive accuracy of crucial hydrological processes important for flood risk assessment.