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This is a Literature Review study.

September 16, 2025WaterOpen Access

Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review

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Authors

JGJerome G. GacuKyoto University CMCris Edward F. MonjardinMapúa University RMRonald Gabriel T. MangulabnanMapúa University

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Implication

This review explores advancements in AI for streamflow prediction in ungauged watersheds, suggesting methods to enhance model performance and transferability.

Key Points

AI-based models consistently outperform traditional models in predicting streamflow, improving accuracy in ungauged watersheds.
Long Short-Term Memory networks and hybrid frameworks significantly enhance model performance across diverse climates and physiographic conditions.
Research highlights the need for standard evaluation protocols to enhance model transferability and reproducibility.
Future efforts should focus on developing physics-informed AI frameworks and open-source tools for robust streamflow forecasting.

Cite This Study

Gacu et al. (2025) studied this question.

synapsesocial.com/papers/68d454bb31b076d99fa59c3e https://doi.org/10.3390/w17182722

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Implication

This review explores advancements in AI for streamflow prediction in ungauged watersheds, suggesting methods to enhance model performance and transferability.

Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review

Discussion

Member takes

Implication

Key Points

Cite This Study

Application of Artificial Intelligence in Hydrological Modeling for Streamflow Prediction in Ungauged Watersheds: A Review

Discussion

Member takes

Implication

Key Points

Cite This Study

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