74 citations as recorded by crossref.

1. A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm K. Rahman & S. Shang 10.3390/rs12244009
2. Multi-source precipitation estimation using machine learning: Clarification and benchmarking Y. Xu et al. 10.1016/j.jhydrol.2024.131195
3. Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number K. Jahan et al. 10.3390/land10050452
4. IMERG BraMaL: An improved gridded monthly rainfall product for Brazil based on satellite‐based IMERG estimates and machine learning techniques E. Freitas et al. 10.1002/joc.8562
5. Artificial Intelligence-Based Techniques for Rainfall Estimation Integrating Multisource Precipitation Datasets R. Khan & M. Bhuiyan 10.3390/atmos12101239
6. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
7. Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy C. Chen et al. 10.5194/hess-25-5667-2021
8. Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique M. Ahmed & L. Lin 10.1016/j.jhydrol.2021.126213
9. Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data J. Chancay & E. Espitia-Sarmiento 10.3390/rs13214446
10. The Enhanced Management of Water Resources Improves Ecosystem Services in a Typical Arid Basin J. Guo et al. 10.3390/su12218802
11. Global River Monitoring Using Semantic Fusion Networks Z. Wei et al. 10.3390/w12082258
12. Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms S. Ezzati et al. 10.1016/j.jenvman.2023.117714
13. 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
14. Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates J. Tan et al. 10.1016/j.jhydrol.2021.126270
15. Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India V. Kolluru et al. 10.3390/rs12183013
16. Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac 10.5194/esd-12-1253-2021
17. Assessment of precipitation error propagation in multi-model global water resource reanalysis M. Ehsan Bhuiyan et al. 10.5194/hess-23-1973-2019
18. Global component analysis of errors in three satellite-only global precipitation estimates H. Chen et al. 10.5194/hess-25-3087-2021
19. Uncertainty estimation of machine learning spatial precipitation predictions from satellite data G. Papacharalampous et al. 10.1088/2632-2153/ad63f3
20. Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables N. Beikahmadi et al. 10.3390/hydrology10060128
21. A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin M. Abdallah et al. 10.5194/hess-28-1147-2024
22. The importance of short lag-time in the runoff forecasting model based on long short-term memory X. Chen et al. 10.1016/j.jhydrol.2020.125359
23. Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach L. Zhang et al. 10.1016/j.jhydrol.2021.125969
24. Reliable prediction of the discharge coefficient of triangular labyrinth weir based on soft computing techniques S. Seyedian et al. 10.1016/j.flowmeasinst.2023.102403
25. Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm X. Zhu et al. 10.1007/s13351-023-2171-1
26. An approach for improving the capability of a coupled meteorological and hydrological model for rainfall and flood forecasts H. Nguyen & D. Bae 10.1016/j.jhydrol.2019.124014
27. An Assimilating Model Using Broad Learning System for Incorporating Multi‐Source Precipitation Data With Environmental Factors Over Southeast China Y. Zhou et al. 10.1029/2021EA002043
28. An assessment of gridded precipitation products over High Mountain Asia I. Dollan et al. 10.1016/j.ejrh.2024.101675
29. Advanced wind speed prediction using convective weather variables through machine learning application B. Md Abul Ehsan et al. 10.1016/j.acags.2019.100002
30. A global-scale evaluation of extreme event uncertainty in the <i>eartH2Observe</i> project T. Marthews et al. 10.5194/hess-24-75-2020
31. Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin M. Bhuiyan et al. 10.3390/forecast2030014
32. Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products G. Papacharalampous et al. 10.1016/j.mlwa.2024.100615
33. Characterizing Hydrologic Vulnerability under Nonstationary Climate and Antecedent Conditions Using a Process-Informed Stochastic Weather Generator S. Rahat et al. 10.1061/(ASCE)WR.1943-5452.0001557
34. A Monte Carlo-based multi-objective optimization approach to merge different precipitation estimates for land surface modeling A. Hazra et al. 10.1016/j.jhydrol.2018.12.039
35. Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles H. Tyralis et al. 10.1109/JSTARS.2023.3297013
36. Assessment of a Gauge-Radar-Satellite Merged Hourly Precipitation Product for Accurately Monitoring the Characteristics of the Super-Strong Meiyu Precipitation over the Yangtze River Basin in 2020 Z. Pang et al. 10.3390/rs13193850
37. Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique Y. Derin et al. 10.1109/TGRS.2020.3038343
38. Evaluating the performance of bias-corrected IMERG satellite rainfall estimates for hydrological simulation over the Upper Bhima River basin, India S. Nandi & M. Janga Reddy 10.1080/10106049.2022.2101695
39. Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables X. Zeng et al. 10.3390/atmos12121578
40. Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery M. Bhuiyan et al. 10.3390/jimaging6090097
41. Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration F. Yang et al. 10.3390/su12041525
42. A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin P. Das et al. 10.1038/s41598-024-61520-6
43. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data S. Das et al. 10.1038/s41597-024-03756-5
44. Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal B. Nepal et al. 10.3390/atmos12020254
45. Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan K. Rahman et al. 10.3390/rs13091662
46. Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region W. Li et al. 10.3390/w13192789
47. Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network J. Chen & Y. Wang 10.3390/w12051281
48. Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework Y. Zhou et al. 10.3390/rs13061057
49. Streamflow trends of the Pyrenees using observations and multi-model approach (1980–2013) R. Clavera-Gispert et al. 10.1016/j.ejrh.2023.101322
50. Blending high-resolution satellite rainfall estimates over urban catchment using Bayesian Model Averaging approach W. Asfaw et al. 10.1016/j.ejrh.2022.101287
51. Advancement of weather-related crash prediction model using nonparametric machine learning algorithms A. Mondal et al. 10.1007/s42452-020-03196-x
52. A synthetic experiment to investigate the potential of assimilating LAI through direct insertion in a land surface model A. Rahman et al. 10.1016/j.hydroa.2020.100063
53. Precipitation assimilation from gauge and satellite products by a Bayesian method with Gamma distribution Y. Chiang et al. 10.1080/01431161.2020.1823037
54. A streamflow-oriented ranking-based methodological framework to combine multiple precipitation datasets across large river basins J. Wong et al. 10.1016/j.jhydrol.2021.127174
55. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
56. Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability M. Avand et al. 10.1016/j.jhydrol.2020.125663
57. Year-ahead predictability of South Asian Summer Monsoon precipitation N. Krakauer 10.1088/1748-9326/ab006a
58. Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods S. Seyedian & O. Kisi 10.2478/johh-2023-0043
59. An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine A. Banerjee et al. 10.3390/rs12040709
60. Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine N. Sazib et al. 10.3390/w12051371
61. Precipitation data merging via machine learning: Revisiting conceptual and technical aspects P. Kossieris et al. 10.1016/j.jhydrol.2024.131424
62. A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchment A. Kumar et al. 10.1016/j.jhydrol.2021.126046
63. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
64. Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. 10.1016/j.jhydrol.2023.130176
65. Accuracy of conventional fusion algorithms for precipitation estimates across the Chinese mainland Q. Jiang et al. 10.2166/hydro.2023.111
66. Accounting for Non-Stationary Relationships between Precipitation and Environmental Variables for Downscaling Monthly TRMM Precipitation in the Upper Indus Basin Y. Wang et al. 10.3390/rs15174356
67. On the verification of ensemble precipitation forecasts over the Godavari River basin V. Manikanta et al. 10.1016/j.jhydrol.2022.128794
68. Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models F. Banadkooki et al. 10.3390/su11236681
69. The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model A. Rahman et al. 10.3390/rs14030437
70. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
71. A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia Y. Mei et al. 10.1029/2020WR027472
72. Machine Learning–Based Blending of Satellite and Reanalysis Precipitation Datasets: A Multiregional Tropical Complex Terrain Evaluation M. Ehsan Bhuiyan et al. 10.1175/JHM-D-19-0073.1
73. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions W. Dorigo et al. 10.1016/j.rse.2017.07.001
74. Improving Ensemble Forecasting Using Total Least Squares and Lead-Time Dependent Bias Correction A. Jabbari & D. Bae 10.3390/atmos11030300