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

20 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
3. A deep learning-based hybrid model of global terrestrial evaporation A. Koppa et al. 10.1038/s41467-022-29543-7
4. Global patterns and key drivers of stream nitrogen concentration: A machine learning approach R. Sheikholeslami & J. Hall 10.1016/j.scitotenv.2023.161623
5. Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network M. Hashemi et al. 10.3390/make6030092
6. Recent global decline in rainfall interception loss due to altered rainfall regimes X. Lian et al. 10.1038/s41467-022-35414-y
7. Multi‐Task Deep Learning of Daily Streamflow and Water Temperature J. Sadler et al. 10.1029/2021WR030138
8. Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models D. Machalek et al. 10.1016/j.egyai.2022.100172
9. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran) R. Shakeri et al. 10.1007/s11356-023-29522-5
10. Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning Z. Li et al. 10.1016/j.scitotenv.2024.176673
11. Multiagent System and Rainfall-Runoff Model in Hydrological Problems: A Systematic Literature Review B. Leitzke & D. Adamatti 10.3390/w13243643
12. Diagnosing modeling errors in global terrestrial water storage interannual variability H. Lee et al. 10.5194/hess-27-1531-2023
13. Calibrating global hydrological models with GRACE TWS: does river storage matter? T. Trautmann et al. 10.1088/2515-7620/acece5
14. 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. 10.5194/hess-25-1671-2021
15. Integrated approach for optimizing groundwater monitoring systems using evolutionary algorithms W. Mahmod et al. 10.1080/02626667.2021.1968404
16. A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir P. Nguyen et al. 10.1002/rvr2.72
17. 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
18. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
19. Towards hybrid modeling of the global hydrological cycle B. Kraft et al. 10.5194/hess-26-1579-2022
20. 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