68 citations as recorded by crossref.

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3. Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning G. Mestrallet https://doi.org/10.3390/cancers16020408
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7. A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions T. Luo et al. https://doi.org/10.3390/rs17091566
8. Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador F. Santos et al. https://doi.org/10.1371/journal.pone.0281869
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19. Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process N. Syakira et al. https://doi.org/10.3390/w15122195
20. Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data G. Papacharalampous et al. https://doi.org/10.3390/w15040634
21. Assessing the Accuracy of Gridded Precipitation Products in the Campania Region, Italy M. Shazil et al. https://doi.org/10.3390/w17172585
22. Enhanced error correction and spatial downscaling of precipitation and air temperature in the middle and low reaches of the Yangtze River using a random forest model with the Sokol method Y. Gong et al. https://doi.org/10.1007/s12145-025-01933-7
23. A Comparative Assessment of Advanced Bias Correction Methods for GPM IMERG in Banjir Kanal Timur Watershed R. Lufira et al. https://doi.org/10.48084/etasr.15484
24. Study on the Spatial Distribution Patterns and Driving Forces of Rainstorm-Induced Flash Flood in the Yarlung Tsangpo River Basin F. He et al. https://doi.org/10.3390/rs17081393
25. Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale G. Papacharalampous et al. https://doi.org/10.3390/hydrology10020050
26. Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation F. Zhou et al. https://doi.org/10.3390/app132111806
27. High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework M. Waleed & M. Sajjad https://doi.org/10.1016/j.ijdrr.2025.105442
28. Cascaded Downscaling–Calibration Networks for Satellite Precipitation Estimation Y. Jing et al. https://doi.org/10.1109/LGRS.2022.3214083
29. Advanced stepwise machine learning integration of near-real-time precipitation products in China's flood-prone basins L. Liu & H. Lei https://doi.org/10.1016/j.atmosres.2025.108197
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31. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. https://doi.org/10.1109/JSTARS.2023.3297013
32. Temporal and spatial variations in precipitation and the distribution of extreme precipitation events in the South China Sea based on GPM and CMIP6 data J. Yan et al. https://doi.org/10.1007/s00382-025-07788-4
33. Locally varying geostatistical machine learning for spatial prediction F. Fouedjio & E. Arya https://doi.org/10.1016/j.aiig.2024.100081
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35. High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences K. Alsafadi et al. https://doi.org/10.3390/rs15092435
36. CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century K. Wang et al. https://doi.org/10.3390/atmos15040434
37. An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia S. Das et al. https://doi.org/10.1016/j.jhydrol.2024.131341
38. Global open‐access DEM vertical elevation and along track neighbouring structure evaluations in the Tibetan Plateau using ICESat‐2 ATL03 points J. Chen et al. https://doi.org/10.1002/esp.6062
39. Unravelling and improving the potential of global discharge reanalysis dataset in streamflow estimation in ungauged basins L. Liu et al. https://doi.org/10.1016/j.jclepro.2023.138282
40. Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model S. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.127718
41. Added value of merging techniques in precipitation estimates relative to gauge-interpolation algorithms of varying complexity Y. Hu & L. Zhang https://doi.org/10.1016/j.jhydrol.2024.132214
42. Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets S. Huang et al. https://doi.org/10.1016/j.rse.2024.114378
43. INTRAGRO: A machine learning approach to predict future growth of trees under climate change S. Aryal et al. https://doi.org/10.1002/ece3.10626
44. Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method L. Wang et al. https://doi.org/10.1016/j.scitotenv.2024.174866
45. Objectivization of an expert assessment framework for drought monitoring H. Xu et al. https://doi.org/10.1016/j.jhydrol.2025.134592
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47. Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity C. Chen et al. https://doi.org/10.1016/j.jhydrol.2025.133223
48. SMPD-MERG: A Hybrid Downscaling Model for High-Resolution Daily Precipitation Estimation via Merging Surface Soil Moisture and Multisource Precipitation Data K. He et al. https://doi.org/10.1109/TGRS.2025.3561253
49. Evaluating Linear Scaling and Random Forest Bias Correction of GPM-IMERG V7 Rainfall in Moluccas Islands Watersheds H. Tuasikal et al. https://doi.org/10.48084/etasr.17008
50. Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data - a machine learning approach S. Eltazarov et al. https://doi.org/10.1080/20964471.2023.2196830
51. Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients N. Zhao & X. Wu https://doi.org/10.1016/j.jhydrol.2023.130367
52. Novel Parameter Calibration Method of WRF-Hydro in Ungauged Areas Combining Satellite-Based Soil Moisture and Multisource Meteorological Data Q. Zhao et al. https://doi.org/10.1007/s11269-025-04336-x
53. Assessing the effectiveness of ANN model in spatial downscaling of d4PDF hourly precipitation data: a case study in Japan I. Khateeb et al. https://doi.org/10.1007/s44367-025-00003-5
54. Enhancing Machine Learning-Based GPP Upscaling Error Correction: An Equidistant Sampling Method with Optimized Step Size and Intervals Z. Wang et al. https://doi.org/10.3390/rs18010023
55. Advancing Satellite-Derived Precipitation Downscaling in Data-Sparse Area Through Deep Transfer Learning H. Zhu & Q. Zhou https://doi.org/10.1109/TGRS.2024.3367332
56. Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction C. Yu et al. https://doi.org/10.1016/j.jhydrol.2023.129560
57. Enhancing drought monitoring through spatial downscaling: A geographically weighted regression approach using TRMM 3B43 precipitation in the Urmia Lake Basin S. Choursi et al. https://doi.org/10.1007/s12145-024-01324-4
58. A downscaling-fusion framework based on machine learning to improve daily precipitation estimates in the dry-hot valley of southwest China J. Feng & B. Liu https://doi.org/10.1016/j.ejrh.2026.103537
59. Research on Random Forest-Based Downscaling Inversion Techniques for Numerical Precipitation Prediction Guided by Integrated Physical Mechanisms H. Liao et al. https://doi.org/10.3390/w18050574
60. Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019 X. Yan et al. https://doi.org/10.1038/s41597-024-03684-4
61. Evaluation Method of Severe Convective Precipitation Based on Dual-Polarization Radar Data Z. Tang et al. https://doi.org/10.3390/w16081136
62. Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression Z. Ferreira et al. https://doi.org/10.1080/22797254.2023.2260092
63. Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation Y. Li et al. https://doi.org/10.1080/17538947.2023.2203953
64. Comparative Study on the Different Downscaling Methods for GPM Products in Complex Terrain Areas J. Liu et al. https://doi.org/10.3390/earth6040129
65. A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network M. Sun et al. https://doi.org/10.1109/JSTARS.2024.3373884
66. Mapping landslide susceptibility with the consideration of spatial heterogeneity and factor optimization C. Chen et al. https://doi.org/10.1007/s11069-024-06955-w
67. Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models S. Medrano et al. https://doi.org/10.3390/atmos14091349
68. Integrating random forest-based regression kriging for analyzing spatial variability of rainfall in arid and semi-arid regions M. Manaf et al. https://doi.org/10.1038/s41598-026-36074-4