31 citations as recorded by crossref.

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3. 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
4. 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
5. An explainable two-stage machine learning approach for precipitation forecast A. Senocak et al. 10.1016/j.jhydrol.2023.130375
6. 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
7. Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
8. A deep learning-based framework for multi-source precipitation fusion K. Gavahi et al. 10.1016/j.rse.2023.113723
9. 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
10. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
11. A novel error decomposition and fusion framework for daily precipitation estimation based on near-real-time satellite precipitation product and gauge observations J. Shi et al. 10.1016/j.jhydrol.2024.131715
12. Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China Z. Li et al. 10.3390/atmos15111315
13. High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration M. Putra et al. 10.3390/s24155030
14. An Integrated Framework for Spatiotemporally Merging Multi-Sources Precipitation Based on F-SVD and ConvLSTM S. Sheng et al. 10.3390/rs15123135
15. Multi-source precipitation estimation using machine learning: Clarification and benchmarking Y. Xu et al. 10.1016/j.jhydrol.2024.131195
16. Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models S. Medrano et al. 10.3390/atmos14091349
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20. 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
21. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
22. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
23. 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
24. Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity Y. Hu & L. Zhang 10.1016/j.jhydrol.2024.132214
25. 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
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27. Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data H. Lei et al. 10.1016/j.ecoinf.2024.102755
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29. Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale W. Qi et al. 10.3390/w16111553
30. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al. 10.3390/rs15082180
31. Accuracy of conventional fusion algorithms for precipitation estimates across the Chinese mainland Q. Jiang et al. 10.2166/hydro.2023.111