14 citations as recorded by crossref.

1. Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model N. Attar et al. https://doi.org/10.3390/app10020571
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4. Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E. Snieder et al. https://doi.org/10.5194/hess-25-2543-2021
5. Interpretation of a Machine Learning Model for Short-Term High Streamflow Prediction S. López-Chacón et al. https://doi.org/10.3390/earth6030064
6. Advancing flood early warning systems: ensemble learning-based classifiers for urban flood forecasting E. Snieder et al. https://doi.org/10.2166/hydro.2025.218
7. A field and modeling study of subsurface stormflow for Huanggou Hillslope Y. Song et al. https://doi.org/10.1016/j.ejrh.2024.101683
8. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. https://doi.org/10.3390/w15112020
9. An Autoregressive Graph Convolutional Long Short-Term Memory Hybrid Neural Network for Accurate Prediction of COVID-19 Cases M. Ntemi et al. https://doi.org/10.1109/TCSS.2022.3167856
10. Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds R. Vilanova et al. https://doi.org/10.1016/j.compag.2019.105080
11. Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts? M. De Santi et al. https://doi.org/10.1371/journal.pwat.0000040
12. Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. https://doi.org/10.1080/02626667.2024.2426720
13. Study on the Classification of Urban Waterlogging Rainstorms and Rainfall Thresholds in Cities Lacking Actual Data B. Ma et al. https://doi.org/10.3390/w12123328
14. Low-flow estimation beyond the mean – expectile loss and extreme gradient boosting for spatiotemporal low-flow prediction in Austria J. Laimighofer et al. https://doi.org/10.5194/hess-26-4553-2022