12 citations as recorded by crossref.

1. Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model G. Seo & J. Ahn 10.3390/atmos14071057
2. Comparison of bias-corrected multisatellite precipitation products by deep learning framework X. Le et al. 10.1016/j.jag.2022.103177
3. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins K. Ma et al. 10.1007/s11442-024-2235-x
4. Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia T. Ukumo et al. 10.1108/WJE-07-2021-0410
5. Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China X. Li et al. 10.3390/rs13050866
6. Quantile-based Bayesian Model Averaging approach towards merging of precipitation products K. Yumnam et al. 10.1016/j.jhydrol.2021.127206
7. Preface: Advances in extreme value analysis and application to natural hazards Y. Hamdi et al. 10.5194/nhess-21-1461-2021
8. Performance Comparison of Bias-Corrected Satellite Precipitation Products by Various Deep Learning Schemes X. Le et al. 10.1109/TGRS.2023.3299234
9. An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5 M. Hasan et al. 10.3390/w16050745
10. Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed, northwest China H. Wang et al. 10.1080/17538947.2022.2083248
11. Performance evaluation of raw and bias-corrected ERA5 precipitation data with respect to extreme precipitation analysis: case study in Upper Jhelum Basin, South Asia R. Ansari & G. Grossi 10.1007/s00704-022-04239-6
12. Development of Multi-Source Weighted-Ensemble Precipitation: Influence of bias correction based on recurrent convolutional neural networks Y. Kao et al. 10.1016/j.jhydrol.2024.130621