22 citations as recorded by crossref.

1. Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models S. Medrano et al. 10.3390/atmos14091349
2. Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation C. Chen et al. 10.1016/j.jhydrol.2024.130919
3. Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al. 10.1016/j.atmosres.2024.107385
4. Detection and Assessment of Changing Drought Events in China in the Context of Climate Change Based on the Intensity–Area–Duration Algorithm Y. Ren et al. 10.3390/land12101820
5. Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction C. Yu et al. 10.1016/j.jhydrol.2023.129560
6. Unravelling and improving the potential of global discharge reanalysis dataset in streamflow estimation in ungauged basins L. Liu et al. 10.1016/j.jclepro.2023.138282
7. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
8. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
9. Comprehensive evaluation of IMERG, ERA5-Land and their fusion products in the hydrological simulation of three karst catchments in Southwest China Y. Chang et al. 10.1016/j.ejrh.2024.101671
10. An explainable two-stage machine learning approach for precipitation forecast A. Senocak et al. 10.1016/j.jhydrol.2023.130375
11. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. 10.3390/hydrology10020050
12. Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
13. A deep learning-based framework for multi-source precipitation fusion K. Gavahi et al. 10.1016/j.rse.2023.113723
14. Quantifying the Reliability and Uncertainty of Satellite, Reanalysis, and Merged Precipitation Products in Hydrological Simulations over the Topographically Diverse Basin in Southwest China H. Lei et al. 10.3390/rs15010213
15. Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China C. Mo et al. 10.1016/j.atmosres.2023.107116
16. Assessing satellite and reanalysis-based precipitation products in cold and arid mountainous regions Y. Yang et al. 10.1016/j.ejrh.2023.101612
17. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
18. Improving near-real-time satellite precipitation products through multistage modified schemes C. Meng et al. 10.1016/j.atmosres.2023.106875
19. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. 10.3390/rs15082180
20. An Integrated Framework for Spatiotemporally Merging Multi-Sources Precipitation Based on F-SVD and ConvLSTM S. Sheng et al. 10.3390/rs15123135
21. Accuracy of conventional fusion algorithms for precipitation estimates across the Chinese mainland Q. Jiang et al. 10.2166/hydro.2023.111
22. Multi-source precipitation estimation using machine learning: Clarification and benchmarking Y. Xu et al. 10.1016/j.jhydrol.2024.131195