From hydrological laws to catchment models: A process-based model matches deep-learning accuracy on three continents
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Xilin Xia · arXiv: 2611.06382v1 · Summary page
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Physical laws give neural networks a close rival
A catchment model built around conservation, dissipation and rules for combining unresolved physical variation approached or exceeded the accuracy of neural networks in tests across Britain, the USA and Australia. Rather than replacing physics with a daily neural network, the researchers refined a compact process-based model through an AI-agent-assisted loop, addressing observed shortcomings with hypotheses from the hydrological literature. The resulting CaRT+ model has fourteen states and 35 parameters per catchment. Its individual models were competitive with LSTMs in held-out years, although LSTM ensembles retained an advantage in Britain and the USA. On Australian catchments unused in development, individual CaRT+ models outperformed the tested individual LSTMs. Beyond hydrology, the result challenges the assumption that predictive accuracy necessarily requires abandoning structured scientific models: better representations of unresolved processes may close much of the gap.
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