Synergizing Intuitive Physics and Big Data in Deep Learning: Can We Obtain Process Insights While Maintaining State‐Of‐The‐Art Hydrological Prediction Capability?

Abstract Artificial intelligence (AI) methods have created insurmountable performance in prediction tasks for geoscientific problems yet are unable to derive process insights and answer specific scientific questions. The geoscience community faces a dilemma of reconciling process comprehension with high predictive accuracy. Here we introduce a deep process learning (DPL) approach empowering neural networks to deduce intrinsic processes from observable data, wherein the intuitive physics of geosystems is directly coupled within the deep learning (DL) architecture as structural prior. We aim to incorporate as raw common concepts as possible as macroscopic guidance: on the one hand, to reduce interference with DL's data adaptability. On the other hand, to allow the information flow of the model to converge along specific paths toward the target output, thus enabling the potential to gain process insights with limited supervision. Illustrating its application to precipitation‐runoff modeling across the USA, DPL yields an ensemble median Nash‐Sutcliffe efficiency of 0.758 and Kling‐Gupta efficiency of 0.778 with robust transferability, compared to 0.762 and 0.751 for the state‐of‐the‐art DL model. The good match between internal representations of DPL and independent data sets of snow water equivalent and evapotranspiration, along with its superior capability for catchment water budget closures, demonstrates proficient process mastery. The study also highlights beneficial synergies from large‐scale data collaboration, promoting the organic unity of process understanding and predictive performance. This work shows a promising avenue for learning processes from big data and will benefit geoscientific domains that remain concerned with process clarity in the era of AI.