89 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.
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.
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.
4. An explainable predictive approach for investigation of greenhouse gas emissions in maritime canada's potato agriculture M. Jamei et al.
5. Soft-cooperation via data sharing eases transboundary conflicts in the Lancang-Mekong River Basin J. Gao et al.
6. Comparison of multi-factor spatial downscaling models for high-resolution LST estimation in mountainous and hilly open-pit mines H. Li et al.
7. Near-Real-Time Satellite Precipitation Data Ingestion into Peak Runoff Forecasting Models P. Muñoz et al.
8. Cutting-edge computational approaches to plant phenotyping V. Kurumayya
9. 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.
10. Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming L. Gu et al.
11. Daily suspended sediment simulation using machine learning approach D. Kumar et al.
12. An evaluation framework for input variable selection algorithms for environmental data-driven models S. Galelli et al.
13. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al.
14. An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models R. Taormina et al.
15. An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products Y. Zhang et al.
16. Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees Algorithm S. Wu et al.
17. Deep-Learning Approach to the Detection and Localization of Cyber-Physical Attacks on Water Distribution Systems R. Taormina & S. Galelli
18. High-Speed Motion Analysis-Based Machine Learning Models for Prediction and Simulation of Flyrock in Surface Mines R. Mishra et al.
19. A framework to characterize, model, and optimize water-energy systems: case study of a novel HDH system M. Elhashimi-Khalifa & B. Abbasi
20. Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values H. Errousso et al.
21. Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique O. Kombo et al.
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.
23. Optimized Rainfall Imputation Using ERA5-Land and Tree-Based Machine Learning: A Scalable Framework for Data-Sparse Regions S. Pinthong et al.
24. Review on the Change Trend, Attribution Analysis, Retrieval, Simulation, and Prediction of Lake Surface Water Temperature T. Jia et al.
25. High‐Performance Integrated Control of water quality and quantity in urban water reservoirs S. Galelli et al.
26. Inverse modelling of pyrolization kinetics with ensemble learning methods P. Lauer et al.
27. The Possible Incoming Runoff Under Extreme Rainfall Event in the Fenhe River Basin S. Jian et al.
28. Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020 J. Zhou et al.
29. 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.
30. A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications Y. Zhang et al.
31. Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades S. Condran et al.
32. Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models B. Bharti et al.
33. Critical factors for the use of machine learning to predict lake surface water temperature A. Yousefi & M. Toffolon
34. Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds K. Li et al.
35. A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing M. Rahnamay Naeini et al.
36. Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data S. Denaro et al.
37. Near-real-time satellite precipitation data ingestion into peak runoff forecasting models P. Muñoz et al.
38. Tree‐based iterative input variable selection for hydrological modeling S. Galelli & A. Castelletti
39. Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information T. Yang et al.
40. Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods K. Whan & M. Schmeits
41. River ice breakup timing prediction through stacking multi-type model trees W. Sun
42. 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.
43. Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al.
44. Simulating Hydropower Discharge using Multiple Decision Tree Methods and a Dynamical Model Merging Technique T. Yang et al.
45. Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings A. Mohammed et al.
46. Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra
47. Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches Y. Zhang et al.
48. 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.
49. Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation H. Nguyen et al.
50. Estimating daily reference evapotranspiration with reduced data input using ensemble learning models in arid and humid regions of China Q. Wei et al.
51. Deep Neural Network Initialization With Decision Trees K. Humbird et al.
52. Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling K. Li et al.
53. A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem M. Giuliani et al.
54. A machine learning based acceleration of segregated pressure correction algorithms for incompressible fluid flow Y. Deng et al.
55. Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement L. Wang et al.
56. New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition S. Agab & F. Chelali
57. A framework for coupling explanation and prediction in hydroecological modelling B. Surridge et al.
58. Modular conceptual modelling approach and software for river hydraulic simulations V. Wolfs et al.
59. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al.
60. Identifying watershed predictors of surface water quality through iterative input selection L. Mejía & M. Barrios
61. Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al.
62. Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study N. Eligüzel et al.
63. A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment H. Zia et al.
64. Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador P. Muñoz et al.
65. Groundwater level prediction in urban karst regions: The comparison of ensemble learning and deep learning models by considering climate conditions X. Song et al.
66. Seasonality of mean flows as a potential tool for the assessment of ecological processes: Mountain rivers, Polish Carpathians A. Radecki-Pawlik et al.
67. Development of Multi-Inflow Prediction Ensemble Model Based on Auto-Sklearn Using Combined Approach: Case Study of Soyang River Dam S. Lee et al.
68. Enhancing runoff forecasting through the integration of satellite precipitation data and hydrological knowledge into machine learning models P. Muñoz et al.
69. Lag response of groundwater to changes in water and sediment characteristics in the lower Yellow River, China M. Lu et al.
70. Evolution and Periodicity of Watershed-scale Runoff: Insight from the Spatiotemporal Variety in Intensive Anthropogenic Activities Region B. Mao et al.
71. Ensemble machine learning paradigms in hydrology: A review M. Zounemat-Kermani et al.
72. 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.
73. 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.
74. Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al.
75. Assessment of the gully erosion susceptibility using three hybrid models in one small watershed on the Loess Plateau Z. Wang et al.
76. Global vegetation dynamics under decreased terrestrial water storage: Insights into water stress response Y. Yang et al.
77. Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning N. Chaney et al.
78. A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven
79. Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks H. Erdal & İ. Karahanoğlu
80. Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector L. Ugur et al.
81. Predictive Analysis Using Web Scraping for the Real Estate Market in Gaziantep A. ÜZÜMCÜ & N. ELİGÜZEL
82. Effect of Catchment-Scale Green Roof Deployment on Stormwater Generation and Reuse in a Tropical City P. Schmitter et al.
83. Modeling of H2S solubility in ionic liquids using deep learning: A chemical structure-based approach S. Mousavi et al.
84. Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation A. Farag
85. Deep and machine learning for daily streamflow estimation: a focus on LSTM, RFR and XGBoost Ö. Terzi et al.
86. 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.
87. Effectiveness of improved bootstrap aggregation (IBA) technique in mapping hydropower to climate variables O. Aiyelokun et al.
88. Future Joint Probability Characteristics of Extreme Precipitation in the Yellow River Basin F. Li et al.
89. ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation L. Zhang et al.