HYBRID MODELING: FUSION OF A DEEP LEARNING APPROACH AND A PHYSICS-BASED MODEL FOR GLOBAL HYDROLOGICAL MODELING

30 citations as recorded by crossref.

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13. Synergizing machine learning and modified physical models for hydrology modeling: A case study of modified SIMHYD and TANK models X. Lei et al. https://doi.org/10.1016/j.jhydrol.2025.134452
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18. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. https://doi.org/10.1029/2021WR030138
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20. An explainable AI approach for interpreting regionally optimized deep neural networks in hydrological prediction F. Hosseini et al. https://doi.org/10.1016/j.jhydrol.2025.133689
21. Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models J. Chen et al. https://doi.org/10.1007/s11269-026-04566-7
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25. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) A. Wunsch et al. https://doi.org/10.5194/hess-25-1671-2021
26. Streamlining Wetland Vegetation Mapping with AlphaEarth Embeddings: Comparable Accuracy to Traditional Methods with Cleaner Maps and Minimal Preprocessing S. Ryan et al. https://doi.org/10.3390/rs18020293
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30. Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) H. Jo et al. https://doi.org/10.1109/JSTARS.2025.3564852