80 citations as recorded by crossref.

1. Infrared Precipitation Retrieval Method Based on Residual Deep Forest C. Bao et al.
2. Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction C. Yu et al.
3. Addressing class imbalance extends the performance frontier of classification–regression satellite-gauge precipitation fusion Z. Yun et al.
4. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al.
5. Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al.
6. Utilizing machine learning to optimize agricultural inputs for improved rice production benefits T. Liu et al.
7. Improving daily precipitation estimation using a double triple collocation-based (DTC) merging framework J. Gu et al.
8. A time-varying weighted merging method for integrating multisource precipitation data considering error variations L. Wei et al.
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. Extreme precipitation in the Yangtze River Basin: Analysis of lagged and asymmetric teleconnections Z. Li et al.
11. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al.
12. Enhancing satellite based precipitation estimates through robust merging frameworks and streamlined ensemble machine learning model development M. Rahimpour et al.
13. A dual-TransUNet deep learning framework for multi-source precipitation merging and improving seasonal and extreme estimates Y. Ye et al.
14. Multisource Precipitation Data Merging Using a Dual-Layer ConvLSTM Model B. Hu et al.
15. Industrial Heritage in China: Spatial Patterns, Driving Mechanisms, and Implications for Sustainable Reuse B. Chen et al.
16. Enhancing flood simulation in data-sparse Niger central hydrological area river basin in Nigeria using machine learning-based data fusion H. Ganiyu et al.
17. Advanced stepwise machine learning integration of near-real-time precipitation products in China's flood-prone basins L. Liu & H. Lei
18. 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.
19. Precipitation downscaling with the integration of multiple precipitation products, land surface data and gauge stations using explainable machine learning algorithms: A case study in the Mediterranean region of Turkiye E. Hisam et al.
20. Seasonal machine learning fusion for improved satellite precipitation estimates: A case study in the upper Ganjiang River, China Y. Chen et al.
21. Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method W. Wang et al.
22. Multi-source precipitation estimation using machine learning: Clarification and benchmarking Y. Xu et al.
23. Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation C. Chen et al.
24. A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, and NWP Data Using Machine Learning over South Korea H. Park et al.
25. Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression H. Chen et al.
26. A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging J. Wang et al.
27. Unravelling and improving the potential of global discharge reanalysis dataset in streamflow estimation in ungauged basins L. Liu et al.
28. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al.
29. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al.
30. Improved Near-Real-Time Precipitation Estimation From Himawari-8 Data and Gauge Observations in the Xiangjiang River Basin Using a Three-Stage Machine Learning Framework S. Yan et al.
31. Short-term rainfall forecasting using multi-task learning and Weibull based postprocessing technique S. Miri et al.
32. The role of precipitation deficit and potential evapotranspiration excess in triggering flash droughts over China L. Zhang et al.
33. Assessing satellite and reanalysis-based precipitation products in cold and arid mountainous regions Y. Yang et al.
34. High-Accuracy Precipitation Fusion via a Two-Stage Machine Learning Approach for Enhanced Drought Monitoring in China’s Drylands W. Wang et al.
35. Quantifying the utility and uncertainty of multi-source fused precipitation products in hydrological simulations Z. You et al.
36. Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data H. Lei et al.
37. Bias correction of satellite precipitation estimates using Mumbai-MESONET observations: A Random Forest approach D. Bisht et al.
38. Simulation of Extreme Flood Events Based on Precipitation Fusion: A Multi-Method Fusion Framework Combining RF and BMA L. Chao et al.
39. Three-step Merging of Daily Multi-satellite Rainfall Estimates Based on Probability Density Function Matching and Dynamic Bayesian Model Averaging Y. Chen et al.
40. Development of a Diagnostic Algorithm for Detecting Freezing Precipitation from ERA5 Dataset: An Adjustment to the Far East M. Pichugin et al.
41. Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity C. Chen et al.
42. Enhancing precipitation merging accuracy in China with machine learning and rain-snow classification Q. Liu et al.
43. Hourly, kilometer-scale precipitation merged from rain gauge, ground-based radar and satellites over east Asia: methods, evaluation and applications H. Xia & K. Wang
44. Accuracy of conventional fusion algorithms for precipitation estimates across the Chinese mainland Q. Jiang et al.
45. Advancing from historical precipitation reconstruction to real-time monitoring: Satellite-gauge fusion with near-real-time data gap-filling accounting for latency periods Z. Yun et al.
46. Precipitation data merging via machine learning: Revisiting conceptual and technical aspects P. Kossieris et al.
47. 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.
48. Improving satellite and reanalysis precipitation estimates in a Himalayan River Basin: a comparative study of bias correction methods with focus on extremes and ensemble method performance H. Tiwari & R. Garg
49. An explainable two-stage machine learning approach for precipitation forecast A. Senocak et al.
50. Refining daily precipitation estimates using machine learning and multi-source data in alpine regions with unevenly distributed gauges H. Lei et al.
51. Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021) D. Paluba et al.
52. Optimization of feature inputs in machine learning-based multi-source precipitation merging Y. Xu et al.
53. A deep learning-based framework for multi-source precipitation fusion K. Gavahi et al.
54. Quantifying the Added Values of a Merged Precipitation Product in Streamflow Prediction over the Central Himalayas S. Guragain et al.
55. Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method S. Zhan et al.
56. A Bayesian model for blending satellite precipitation estimates to enhance drought monitoring in poorly gauged regions V. Raposo et al.
57. Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China Z. Li et al.
58. Machine learning-based precipitation dataset for the Yarlung Zangbo River Basin: Generation, evaluation, and environmental factor analysis H. Zha et al.
59. RainMerge: a two-stage framework for gauge-independent merging of multiple rainfall products S. Shah et al.
60. High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration M. Putra et al.
61. An Integrated Framework for Spatiotemporally Merging Multi-Sources Precipitation Based on F-SVD and ConvLSTM S. Sheng et al.
62. A classification‒regression‒correction (CRC) strategy for precipitation merging with gauge-independent spatial autocorrelation predictors R. Gao et al.
63. Multi-scale assessment and entropy-MCDM framework for evaluating reanalysis precipitation datasets over Indian basins H. Singh & M. Mohanty
64. Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models S. Medrano et al.
65. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data S. Das et al.
66. 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.
67. A Novel Machine Learning-Based Clustering-Merging Method for Improving Extreme Precipitation Estimation M. Rahimpour et al.
68. Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity Y. Hu & L. Zhang
69. 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.
70. Developing a radar-rain gauge hourly blended precipitation dataset for Great Britain using the Gauss Blending Method X. Qiu et al.
71. Reconstruction of high-precision gridded precipitation dataset in the alpine cold regions of the Qilian Mountains: An intelligent technological framework from downscaling to calibration R. Wang et al.
72. Enhanced precipitation estimation in a Himalayan river basin through the fusion of multi-source datasets using various machine learning techniques H. Tiwari et al.
73. Research on Multi-Source Precipitation Fusion Based on Classification and Regression Machine Learning Methods—A Case Study of the Min River Basin in the Eastern Source of the Qinghai–Tibet Plateau S. Liu et al.
74. Improving near-real-time satellite precipitation products through multistage modified schemes C. Meng et al.
75. Comparative analysis of daily precipitation generation using MBLRPM and machine learning approaches for South Korea Y. Chung & M. Um
76. Multisource precipitation data fusion: Generating high-quality precipitation estimates H. Chen et al.
77. Improving Daily Precipitation Estimates through Machine Learning-Based Downscaling, Precipitation Event Classification, and Categorical Merging Z. Yue et al.
78. Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale W. Qi et al.
79. Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude H. Li et al.
80. Multi-source precipitation product fusion strategy based on a novel ensemble validation framework J. Sun et al.