Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin

doi:10.1016/j.envsoft.2025.106592

Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin

2025-06-26

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DOI：10.1016/j.envsoft.2025.106592

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• Hybrid framework integrates MOLOCH meteorological, FEST hydrological, and ensemble ML models. • Real-time correction employs multiple ML algorithms integrated via Stacking. • The framework is specifically designed to enhance hourly runoff forecasting in small, rapid-response river basins. • Extensive evaluation against AR and LSTM models demonstrates the framework's effectiveness.

Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin

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