95 citations as recorded by crossref.

1. Identifying key drivers of harmful algal blooms in a tributary of the Three Gorges Reservoir between different seasons: Causality based on data-driven methods Y. Su et al. https://doi.org/10.1016/j.envpol.2021.118759
2. Machine Learning-Based Models of Industrial Predictive Emissions Monitoring Systems: Framework of Accuracy and Computational Efficiency Trade-Offs With Life Cycle Cost Analysis R. Winarto et al. https://doi.org/10.1109/ACCESS.2026.3661604
3. Flash-Flood Forecasting in an Andean Mountain Catchment—Development of a Step-Wise Methodology Based on the Random Forest Algorithm P. Muñoz et al. https://doi.org/10.3390/w10111519
4. An explainable predictive approach for investigation of greenhouse gas emissions in maritime canada's potato agriculture M. Jamei et al. https://doi.org/10.1016/j.atech.2024.100709
5. Soft-cooperation via data sharing eases transboundary conflicts in the Lancang-Mekong River Basin J. Gao et al. https://doi.org/10.1016/j.jhydrol.2022.127464
6. Comparison of multi-factor spatial downscaling models for high-resolution LST estimation in mountainous and hilly open-pit mines H. Li et al. https://doi.org/10.1016/j.infrared.2023.105085
7. Cutting-edge computational approaches to plant phenotyping V. Kurumayya https://doi.org/10.1007/s11103-025-01582-w
8. DCC-DNN: A deep neural network model to predict the drag coefficients of spherical and non-spherical particles aided by empirical correlations M. Presa-Reyes et al. https://doi.org/10.1016/j.powtec.2024.119388
9. Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming L. Gu et al. https://doi.org/10.1029/2022WR032997
10. Daily suspended sediment simulation using machine learning approach D. Kumar et al. https://doi.org/10.1016/j.catena.2015.11.013
11. An evaluation framework for input variable selection algorithms for environmental data-driven models S. Galelli et al. https://doi.org/10.1016/j.envsoft.2014.08.015
12. Predictive modelling of pavement condition index: a machine learning framework for infrastructure management S. Gowda et al. https://doi.org/10.1080/10298436.2026.2666864
13. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. https://doi.org/10.3390/w11050910
14. An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models R. Taormina et al. https://doi.org/10.1016/j.jhydrol.2016.07.045
15. An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products Y. Zhang et al. https://doi.org/10.3390/rs12244015
16. Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees Algorithm S. Wu et al. https://doi.org/10.3390/rs14205248
17. Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems R. Taormina & S. Galelli https://doi.org/10.1061/(ASCE)WR.1943-5452.0000983
18. High-Speed Motion Analysis-Based Machine Learning Models for Prediction and Simulation of Flyrock in Surface Mines R. Mishra et al. https://doi.org/10.3390/app13179906
19. A framework to characterize, model, and optimize water-energy systems: case study of a novel HDH system M. Elhashimi-Khalifa & B. Abbasi https://doi.org/10.1016/j.desal.2024.117735
20. Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values H. Errousso et al. https://doi.org/10.1007/s13748-022-00291-5
21. Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique O. Kombo et al. https://doi.org/10.3390/hydrology7030059
22. Comparative assessment of empirical random forest family's model in simulating future streamflow in different basin of Sarawak, Malaysia Z. Sa’adi et al. https://doi.org/10.1016/j.jastp.2024.106381
23. Integrating machine learning with experimental data to predict tensile strength of cement pastes with binary cementitious systems A. Khan et al. https://doi.org/10.1016/j.jcomc.2026.100749
24. Optimized Rainfall Imputation Using ERA5-Land and Tree-Based Machine Learning: A Scalable Framework for Data-Sparse Regions S. Pinthong et al. https://doi.org/10.1007/s41748-025-00787-9
25. Review on the Change Trend, Attribution Analysis, Retrieval, Simulation, and Prediction of Lake Surface Water Temperature T. Jia et al. https://doi.org/10.1109/JSTARS.2022.3188788
26. High‐Performance Integrated Control of water quality and quantity in urban water reservoirs S. Galelli et al. https://doi.org/10.1002/2015WR017595
27. Inverse modelling of pyrolization kinetics with ensemble learning methods P. Lauer et al. https://doi.org/10.1016/j.firesaf.2023.103744
28. The Possible Incoming Runoff Under Extreme Rainfall Event in the Fenhe River Basin S. Jian et al. https://doi.org/10.3389/fenvs.2022.812351
29. Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020 J. Zhou et al. https://doi.org/10.5194/essd-16-5171-2024
30. Construction and optimization of machine learning models based on remote sensing inversion of NH3-N and TP pollution in the Qiantang River Basin, China X. Sichen et al. https://doi.org/10.1016/j.envres.2026.124481
31. A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications Y. Zhang et al. https://doi.org/10.3390/app12178654
32. Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades S. Condran et al. https://doi.org/10.1109/ACCESS.2022.3188649
33. Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models B. Bharti et al. https://doi.org/10.2166/nh.2017.153
34. Critical factors for the use of machine learning to predict lake surface water temperature A. Yousefi & M. Toffolon https://doi.org/10.1016/j.jhydrol.2021.127418
35. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al. https://doi.org/10.1016/j.jhydrol.2022.128323
36. A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al. https://doi.org/10.3390/w12092373
37. Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data S. Denaro et al. https://doi.org/10.1016/j.advwatres.2017.02.012
38. Near-real-time satellite precipitation data ingestion into peak runoff forecasting models P. Muñoz et al. https://doi.org/10.1016/j.envsoft.2022.105582
39. Tree‐based iterative input variable selection for hydrological modeling S. Galelli & A. Castelletti https://doi.org/10.1002/wrcr.20339
40. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information T. Yang et al. https://doi.org/10.1002/2017WR020482
41. Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods K. Whan & M. Schmeits https://doi.org/10.1175/MWR-D-17-0290.1
42. Machine learning models with explainable AI for PM2.5 prediction for Urban air quality assessment C. Waghmare et al. https://doi.org/10.1016/j.mex.2026.104217
43. River ice breakup timing prediction through stacking multi-type model trees W. Sun https://doi.org/10.1016/j.scitotenv.2018.07.001
44. Drought modelling by standard precipitation index (SPI) in a semi-arid climate using deep learning method: long short-term memory A. Docheshmeh Gorgij et al. https://doi.org/10.1007/s00521-021-06505-6
45. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. https://doi.org/10.5194/hess-20-2611-2016
46. Simulating Hydropower Discharge using Multiple Decision Tree Methods and a Dynamical Model Merging Technique T. Yang et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001146
47. A machine learning-based framework for predicting hypertension using serum hematological factors M. Moradi et al. https://doi.org/10.1038/s41598-026-57637-5
48. Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings A. Mohammed et al. https://doi.org/10.3390/su13158298
49. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra https://doi.org/10.1007/s11269-024-04029-x
50. Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al. https://doi.org/10.1029/2018WR023325
51. Simulation of Active Layer Thickness Based on Multi-Source Remote Sensing Data and Integrated Machine Learning Models: A Case Study of the Qinghai-Tibet Plateau G. Wang et al. https://doi.org/10.3390/rs17122006
52. Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation H. Nguyen et al. https://doi.org/10.2166/wcc.2023.573
53. Estimating daily reference evapotranspiration with reduced data input using ensemble learning models in arid and humid regions of China Q. Wei et al. https://doi.org/10.1016/j.compag.2025.110548
54. Deep Neural Network Initialization With Decision Trees K. Humbird et al. https://doi.org/10.1109/TNNLS.2018.2869694
55. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al. https://doi.org/10.5194/hess-25-4947-2021
56. Enhancing Karst Spring Discharge Simulation Through a Hybrid XGBoost–BiLSTM Machine Learning Framework M. Eid et al. https://doi.org/10.3390/w18091038
57. A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem M. Giuliani et al. https://doi.org/10.1016/j.envsoft.2014.02.011
58. A machine learning based acceleration of segregated pressure correction algorithms for incompressible fluid flow Y. Deng et al. https://doi.org/10.1016/j.compfluid.2023.106145
59. Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement L. Wang et al. https://doi.org/10.1088/1361-6501/aa57d6
60. New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition S. Agab & F. Chelali https://doi.org/10.1007/s11042-023-14433-x
61. A framework for coupling explanation and prediction in hydroecological modelling B. Surridge et al. https://doi.org/10.1016/j.envsoft.2014.02.012
62. Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al. https://doi.org/10.1016/j.envsoft.2015.05.010
63. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. https://doi.org/10.1080/02626667.2016.1159683
64. Identifying watershed predictors of surface water quality through iterative input selection L. Mejía & M. Barrios https://doi.org/10.1007/s13762-022-04406-2
65. Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. https://doi.org/10.1016/j.jhydrol.2022.127936
66. Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study N. Eligüzel et al. https://doi.org/10.1016/j.aei.2020.101151
67. A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment H. Zia et al. https://doi.org/10.1109/ACCESS.2019.2901218
68. Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador P. Muñoz et al. https://doi.org/10.3390/hydrology8040183
69. Groundwater level prediction in urban karst regions: The comparison of ensemble learning and deep learning models by considering climate conditions X. Song et al. https://doi.org/10.1016/j.uclim.2025.102687
70. Seasonality of mean flows as a potential tool for the assessment of ecological processes: Mountain rivers, Polish Carpathians A. Radecki-Pawlik et al. https://doi.org/10.1016/j.scitotenv.2020.136988
71. Development of Multi-Inflow Prediction Ensemble Model Based on Auto-Sklearn Using Combined Approach: Case Study of Soyang River Dam S. Lee et al. https://doi.org/10.3390/hydrology10040090
72. Enhancing runoff forecasting through the integration of satellite precipitation data and hydrological knowledge into machine learning models P. Muñoz et al. https://doi.org/10.1007/s11069-024-06939-w
73. Lag response of groundwater to changes in water and sediment characteristics in the lower Yellow River, China M. Lu et al. https://doi.org/10.1016/j.jhydrol.2022.128048
74. Evolution and Periodicity of Watershed-scale Runoff: Insight from the Spatiotemporal Variety in Intensive Anthropogenic Activities Region B. Mao et al. https://doi.org/10.1007/s11269-024-04050-0
75. Ensemble machine learning paradigms in hydrology: A review M. Zounemat-Kermani et al. https://doi.org/10.1016/j.jhydrol.2021.126266
76. Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN S. Heddam et al. https://doi.org/10.1016/j.jhydrol.2020.125130
77. Machine learning-based framework to enhance hydrogen flow rate in proton-exchange membrane water electrolysis: From prediction to cell design suggestion S. Lee et al. https://doi.org/10.1016/j.jece.2026.121858
78. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. https://doi.org/10.1016/j.jhydrol.2020.125531
79. Evaluating the Performance of Serial Production Lines Using Tree-Based Machine Learning Methods . Feras Mabrouke et al. https://doi.org/10.31181/rme444
80. Assessment of the gully erosion susceptibility using three hybrid models in one small watershed on the Loess Plateau Z. Wang et al. https://doi.org/10.1016/j.still.2022.105481
81. Global vegetation dynamics under decreased terrestrial water storage: Insights into water stress response Y. Yang et al. https://doi.org/10.1016/j.agrformet.2025.110549
82. Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning N. Chaney et al. https://doi.org/10.1002/2016JD024821
83. Reliability-calibrated explainable machine learning for scientific interpretation under spatial, resampling, and predictor-correlation uncertainty C. Ibebuchi https://doi.org/10.1088/2632-2153/ae8a13
84. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
85. Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks H. Erdal & İ. Karahanoğlu https://doi.org/10.1016/j.asoc.2016.09.010
86. Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector L. Ugur et al. https://doi.org/10.1007/s10614-018-9814-9
87. Predictive Analysis Using Web Scraping for the Real Estate Market in Gaziantep A. ÜZÜMCÜ & N. ELİGÜZEL https://doi.org/10.17798/bitlisfen.1155725
88. Effect of Catchment-Scale Green Roof Deployment on Stormwater Generation and Reuse in a Tropical City P. Schmitter et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000643
89. Modeling of H2S solubility in ionic liquids using deep learning: A chemical structure-based approach S. Mousavi et al. https://doi.org/10.1016/j.molliq.2021.118418
90. Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation A. Farag https://doi.org/10.1038/s41598-025-23166-w
91. Deep and machine learning for daily streamflow estimation: a focus on LSTM, RFR and XGBoost Ö. Terzi et al. https://doi.org/10.2166/wpt.2023.144
92. Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India S. Gupta et al. https://doi.org/10.1016/j.pce.2024.103603
93. Effectiveness of improved bootstrap aggregation (IBA) technique in mapping hydropower to climate variables O. Aiyelokun et al. https://doi.org/10.1007/s42108-020-00105-1
94. Future Joint Probability Characteristics of Extreme Precipitation in the Yellow River Basin F. Li et al. https://doi.org/10.3390/w15223957
95. ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation L. Zhang et al. https://doi.org/10.3390/rs13183703