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

19 citations as recorded by crossref.

1. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
2. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning Ž. Brkić & M. Kuhta 10.3390/su141610447
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10. Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review B. Leitzke & D. Adamatti 10.3390/w13243643
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15. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
16. High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. 10.3389/frwa.2021.701726
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19. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning R. ElGhawi et al. 10.1088/1748-9326/acbbe0