Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes
Abstract:In recent years, Machine Learning (ML) techniques have gained the
attention of the hydrological community for their better predictive
skills. Specifically, ML models are widely applied for streamflow
predictions. However, limited interpretability in the ML models
indicates space for improvement. Leveraging domain knowledge from
conceptual models can aid in overcoming interpretability issues in ML
models. Here, we have developed the Physics Informed Machine Learning
(PIML) model at daily timestep, which account… Show more
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