14 citations as recorded by crossref.

1. An illustration of model agnostic explainability methods applied to environmental data C. Wikle et al. 10.1002/env.2772
2. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
3. Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions C. Schutte et al. 10.2166/hydro.2024.268
4. Streamflow Estimation in a Mediterranean Watershed Using Neural Network Models: A Detailed Description of the Implementation and Optimization A. Oliveira et al. 10.3390/w15050947
5. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
6. Modeling the Streamflow Response to Heatwaves Across Glacierized Basins in Southwestern Canada S. Anderson & V. Radić 10.1029/2023WR035428
7. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
8. Spatiotemporal convolutional long short-term memory for regional streamflow predictions A. Mohammed & G. Corzo 10.1016/j.jenvman.2023.119585
9. Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks X. Yang et al. 10.1007/s11269-023-03731-6
10. Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
11. A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River L. Zhou & L. Kang 10.3390/w15081556
12. A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability S. Chidepudi et al. 10.1016/j.scitotenv.2022.161035
13. Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
14. In-stream <i>Escherichia coli</i> modeling using high-temporal-resolution data with deep learning and process-based models A. Abbas et al. 10.5194/hess-25-6185-2021