8 citations as recorded by crossref.

1. The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management V. Kumar et al. 10.3390/su151310543
2. Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. 10.1007/s11069-023-06341-y
3. Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects D. Dalla Torre et al. 10.1007/s42452-024-05819-z
4. Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion Z. Situ et al. 10.1016/j.jhydrol.2024.130743
5. Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
6. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal 10.1016/j.jhydrol.2024.130907
7. A hybrid rainfall-runoff model: integrating initial loss and LSTM for improved forecasting W. Wang et al. 10.3389/fenvs.2023.1261239
8. Enhancing Flood Susceptibility Modeling: a Hybrid Deep Neural Network with Statistical Learning Algorithms for Predicting Flood Prone Areas M. Ghobadi & M. Ahmadipari 10.1007/s11269-024-03770-7