Advancing AI and Deep Learning Applications in Hydrological Prediction: Insights on Regional Model Development

The application of artificial intelligence and deep learning (DL) in hydrological sciences presents significant challenges and opportunities, particularly in regional and large-scale modeling. Building on the foundational works of Valiela (2000) and Beven (2020)—which underscore the importance of catchment-wise performance evaluation and uniqueness of the place in regional model comparisons—this study investigates nuanced implementation of deep neural networks (DNNs), specifically Long Short-Term Memory (LSTM), for regional rainfall-runoff predictions. Insights from recent advancements in LSTM-based rainfall-runoff modeling (Kratzert et al., 2024) and ensemble learning of catchment-wise regional LSTMs (Hosseini et al., 2024, 2025) emphasize the critical role of network architecture and training strategies.

Findings reveal regionally optimized DNNs with identical neurons (e.g., LSTM cells) but differing architectures (hyperparameters) can exhibit meaningfully distinct behaviors on the same dataset. For instance, one model captured region-wide generalizable patterns by greedily prioritizing overall accuracy in natural basins but underperforming in specific catchments. While another optimized version emphasized on anomalies (e.g., data deficiencies or snow processes) or human-induced influences (regulated flows), leading to improved accuracy in specific locations. Ensemble deep learning, combined with systematic hyperparameter optimization of regional LSTMs, effectively mitigates these discrepancies by synthesizing diverse learning perspectives into robust and accurate predictions, align with “wisdom of the crowd” principle (Surowiecki, 2004). This approach enhances the potential scalability of “one-size-fits-all” large-scale hydrological DNN, advancing the development of high-accuracy regional hydrological models.

Despite computational challenges, the findings underscore the potential of large-scale hydrological models powered by intelligent agents, environment-aware frameworks (Russell & Norvig, 2020), emphasizing the transformative interplay of DL architectures, ensemble strategies, and scalability in AI-driven hydrological modeling.

References

Valiela, I., 2001, Doing Science: Design, Analysis & Communication of Scientific Research, Oxford Uni. Press

Beven, K., 2020, Deep learning, hydrological processes & the uniqueness of place, Hydrol. Process., 34 (16), pp. 3608-3613

Kratzert, F., et al., 2024, HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin, HESS, 28 (17), pp. 4187-4201

Hosseini, F., et al., 2024, Hyperparameter optimization of regional hydrological LSTMs by random search. Jhydrol, 643, 132003, 10.1016/j.jhydrol.2024.132003

Hosseini, F., et al., 2025, Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling. Jhydrol, 646, 132269. 10.1016/j.jhydrol.2024.132269

Surowiecki, J., 2004, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday.

Russell, S., & Norvig, P., 2020. Artificial intelligence: A modern approach. Pearson