45 citations as recorded by crossref.

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4. Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning G. Mestrallet 10.3390/cancers16020408
5. 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
6. CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century K. Wang et al. 10.3390/atmos15040434
7. An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia S. Das et al. 10.1016/j.jhydrol.2024.131341
8. 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
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11. Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China X. Li et al. 10.3389/feart.2023.1128990
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14. INTRAGRO: A machine learning approach to predict future growth of trees under climate change S. Aryal et al. 10.1002/ece3.10626
15. Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method L. Wang et al. 10.1016/j.scitotenv.2024.174866
16. Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting L. Chao et al. 10.1016/j.envsoft.2022.105421
17. Impacts of soil erosion and climate change on the built heritage of the Pambamarca Fortress Complex in northern Ecuador F. Santos et al. 10.1371/journal.pone.0281869
18. An attention mechanism based convolutional network for satellite precipitation downscaling over China Y. Jing et al. 10.1016/j.jhydrol.2022.128388
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20. Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence H. Imanian et al. 10.3390/w15030473
21. Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data H. Zhu et al. 10.3390/rs15102640
22. Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data G. Papacharalampous et al. 10.3390/rs15204912
23. Multiple-point geostatistics-based spatial downscaling of heavy rainfall fields W. Zou et al. 10.1016/j.jhydrol.2024.130899
24. Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients N. Zhao & X. Wu 10.1016/j.jhydrol.2023.130367
25. A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China H. Lei et al. 10.5194/hess-26-2969-2022
26. Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process N. Syakira et al. 10.3390/w15122195
27. Comparison of Machine Learning Algorithms for Merging Gridded Satellite and Earth-Observed Precipitation Data G. Papacharalampous et al. 10.3390/w15040634
28. Advancing Satellite-Derived Precipitation Downscaling in Data-Sparse Area Through Deep Transfer Learning H. Zhu & Q. Zhou 10.1109/TGRS.2024.3367332
29. 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
30. Enhancing drought monitoring through spatial downscaling: A geographically weighted regression approach using TRMM 3B43 precipitation in the Urmia Lake Basin S. Choursi et al. 10.1007/s12145-024-01324-4
31. 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
32. Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019 X. Yan et al. 10.1038/s41597-024-03684-4
33. Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation F. Zhou et al. 10.3390/app132111806
34. Cascaded Downscaling–Calibration Networks for Satellite Precipitation Estimation Y. Jing et al. 10.1109/LGRS.2022.3214083
35. Evaluation Method of Severe Convective Precipitation Based on Dual-Polarization Radar Data Z. Tang et al. 10.3390/w16081136
36. Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P. Jiang et al. 10.1029/2023MS003800
37. Analysing the spatial context of the altimetric error pattern of a digital elevation model using multiscale geographically weighted regression Z. Ferreira et al. 10.1080/22797254.2023.2260092
38. 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. 10.1080/17538947.2023.2203953
39. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
40. A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network M. Sun et al. 10.1109/JSTARS.2024.3373884
41. Locally varying geostatistical machine learning for spatial prediction F. Fouedjio & E. Arya 10.1016/j.aiig.2024.100081
42. Precipitation data merging via machine learning: Revisiting conceptual and technical aspects P. Kossieris et al. 10.1016/j.jhydrol.2024.131424
43. Mapping landslide susceptibility with the consideration of spatial heterogeneity and factor optimization C. Chen et al. 10.1007/s11069-024-06955-w
44. Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models S. Medrano et al. 10.3390/atmos14091349
45. Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data T. Sun et al. 10.1016/j.heliyon.2024.e36368