15 citations as recorded by crossref.

1. Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction W. Liu et al. https://doi.org/10.1155/2022/3672905
2. Machine learning applications in flood forecasting and predictions, challenges, and way-out in the perspective of changing environment V. Kumar et al. https://doi.org/10.3934/environsci.2025004
3. Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes G. Papacharalampous et al. https://doi.org/10.1007/s00477-018-1638-6
4. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683
5. Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications S. Pulla et al. https://doi.org/10.3390/w16070949
6. Deep learning and wavelet packet transform for fault diagnosis in double circuit transmission lines Z. Ali & E. Esmail https://doi.org/10.1038/s41598-025-15583-8
7. Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System W. Liu & C. Chung https://doi.org/10.3390/w6061642
8. Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods Q. Ouyang & W. Lu https://doi.org/10.1007/s11269-017-1832-1
9. Snow glacier melt estimation in tropical Andean glaciers using artificial neural networks V. Moya Quiroga et al. https://doi.org/10.5194/hess-17-1265-2013
10. Reservoir computing for modeling and predicting stream chemistry P. Allsup et al. https://doi.org/10.2166/hydro.2025.213
11. Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework N. Mount et al. https://doi.org/10.5194/hess-17-2827-2013
12. Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies N. Vakitbilir et al. https://doi.org/10.3390/s24248148
13. Improving current forecast by Leveraging Measured Data and numerical models via LiESNs F. Moreno et al. https://doi.org/10.1016/j.envsoft.2025.106556
14. Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants G. Li et al. https://doi.org/10.3390/en81012228
15. Short-term forecasting model for aggregated regional hydropower generation C. Monteiro et al. https://doi.org/10.1016/j.enconman.2014.08.017