36 citations as recorded by crossref.

1. Identifying impacts of global climate teleconnection patterns on land water storage using machine learning I. Kalu et al. 10.1016/j.ejrh.2023.101346
2. Improving estimation capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty H. Jeong et al. 10.1016/j.jhydrol.2024.130942
3. A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches F. Alizadeh et al. 10.2166/wcc.2020.006
4. CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building facades J. Han et al. 10.1007/s12273-021-0837-0
5. Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
6. Prediction of Discharge in a Tidal River Using Artificial Neural Networks H. Hidayat et al. 10.1061/(ASCE)HE.1943-5584.0000970
7. The Delft-FEWS flow forecasting system M. Werner et al. 10.1016/j.envsoft.2012.07.010
8. Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective S. Razavi et al. 10.1002/hyp.14596
9. Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
10. Scenario-based prediction of short-term river stage–discharge process using wavelet-EEMD-based relevance vector machine K. Roushangar & F. Alizadeh 10.2166/hydro.2018.023
11. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
12. Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
13. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
14. Incorporating non-uniformity and non-linearity of hydrologic and catchment characteristics in rainfall–runoff modeling using conceptual, data-driven, and hybrid techniques V. Vidyarthi & A. Jain 10.2166/hydro.2022.088
15. Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane 10.1080/15715124.2016.1203331
16. Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
17. Review of hydrological modelling in the Australian Alps: from rainfall-runoff to physically based models N. Harvey et al. 10.1080/13241583.2024.2343453
18. Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach . Farhad Alizadeh et al. 10.1134/S0097807820010066
19. Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands H. Hidayat et al. 10.5194/hess-21-2579-2017
20. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran M. H. Kashani et al. 10.1016/j.jhydrol.2016.06.028
21. Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
22. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
23. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. 10.1016/j.jhydrol.2016.06.026
24. A systematic review of Muskingum flood routing techniques A. Salvati et al. 10.1080/02626667.2024.2324132
25. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
26. Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China Z. Huo et al. 10.1016/j.jhydrol.2011.11.054
27. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China L. Liu et al. 10.1016/j.jhydrol.2017.08.032
28. Fuzzy Conceptual Hydrological Model for Water Flow Prediction M. Turan & M. Yurdusev 10.1007/s11269-015-1183-8
29. A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
30. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
31. Wavelet analyses of neural networks based river discharge decomposition L. Campozano et al. 10.1002/hyp.13726
32. Improving capability of conceptual modeling of watershed rainfall–runoff using hybrid wavelet-extreme learning machine approach K. Roushangar et al. 10.2166/hydro.2017.011
33. Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific A. Lal & B. Datta 10.1016/j.jenvman.2018.12.054
34. Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
35. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
36. Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels M. Siddiquee & M. Hossain 10.1007/s00521-015-1871-6