101 citations as recorded by crossref.

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2. Evaluation of Snowmelt and Rainfall Erosion in the Total Soil Losses in a Typical Small Watershed in Black Soil Region of Northeast China Z. Ren et al.
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12. Streamflow Forecasting Based on PatchTST, LSTM, and Ensemble Learning Approaches H. Feizi & M. Sattari
13. A comparative evaluation of streamflow prediction using the SWAT and NNAR models in the Meenachil River Basin of Central Kerala, India M. Saranya & V. Vinish
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20. Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China J. Baek et al.
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22. Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model Y. Luo et al.
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24. Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics O. Jaffar et al.
25. Microstructure and mechanical properties evaluation of automotive plate steel based on micromagnetic NDT technologies H. Sheng et al.
26. Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al.
27. Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials M. Ahmad et al.
28. Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights V. Kartal et al.
29. How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology D. Bilolikar et al.
30. Genetic algorithm-assisted an improved AdaBoost double-layer for oil temperature prediction of TBM J. Ren et al.
31. Copula-based method for non-stationarity identification analysis of the dependency structure of hydrological variables X. Bai et al.
32. Global streamflow modelling using process-informed machine learning M. Magni et al.
33. Streamflow forecasting using machine learning for flood management and mitigation in the White Volta basin of Ghana J. Katsekpor et al.
34. Modeling hydrological functioning of karst aquifer systems in Slovenia using geomorphological features and random forest algorithm M. Janža et al.
35. Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al.
36. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach
37. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al.
38. Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research S. Doğanalp & İ. Köz
39. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al.
40. Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al.
41. Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments H. Mushtaq et al.
42. Future Hydrological Drought and Water Sustainability in the Sava River Basin: Machine Learning Projections Under Climate Change Scenarios I. Leščešen et al.
43. Evaluating ecosystem water use efficiency under drought stress: a case study of the Helan Mountain region, northwest China X. Wang et al.
44. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al.
45. Assessing machine learning models for streamflow estimation: a case study in Oued Sebaou watershed (Northern Algeria) Z. Abda et al.
46. Stacking machine learning models versus a locally weighted linear model to generate high-resolution monthly precipitation over a topographically complex area O. Zandi et al.
47. Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems G. Kopsiaftis et al.
48. Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques S. Velivelli et al.
49. Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 2011–2020 in Yunnan, China Z. Zhu et al.
50. Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting J. Liu et al.
51. The relative importance of model type and input features for water supply forecasting in snow-dominated basins of the southwestern US M. Pernat et al.
52. A spatiotemporally differentiated hybrid hydrological modeling strategy with dynamically adaptive runoff generation modes X. Ma et al.
53. Conjunct application of machine learning and game theory in groundwater quality mapping A. Khiavi et al.
54. Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches A. Gogineni et al.
55. A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting M. Danesh et al.
56. Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin S. Tang et al.
57. Hybrid approaches enhance hydrological model usability for local streamflow prediction Y. Du & I. Pechlivanidis
58. Modeling riparian flood plain wetland water richness in pursuance of damming and linking it with a methane emission rate S. Pal & R. Sarda
59. Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin R. Noor et al.
60. A predictive model of velocity for local hydrokinetic power assessment based on remote sensing data A. MacMillan et al.
61. Interpretation of a Machine Learning Model for Short-Term High Streamflow Prediction S. López-Chacón et al.
62. Comparative analysis of machine learning models for water level forecasting in the Karang Mumus River, Indonesia F. Aditama et al.
63. A random forest approach to improve estimates of tributary nutrient loading P. Isles
64. Machine learning-based streamflow forecasting in Colombian hydropower basins at daily and monthly scales L. Pulgarín-Morales et al.
65. What makes a robust calibration period? Insights into the effects of data properties O. Jaffar et al.
66. Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach L. Wang et al.
67. Multi-Variables-Driven Model Based on Random Forest and Gaussian Process Regression for Monthly Streamflow Forecasting N. Sun et al.
68. Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting R. Sarah et al.
69. A Novel and Non-Invasive Approach to Evaluating Soil Moisture without Soil Disturbances: Contactless Ultrasonic System D. Woo et al.
70. Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al.
71. Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions B. Mohammadi et al.
72. An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting W. Fang et al.
73. Enhancing runoff simulation in data-scarce mountainous regions: a coupled SWAT and transferable transformer approach Y. He et al.
74. Localized Browning in Thermokarst-Dominated Landscapes Reverses Regional Greening Trends Under a Warming Climate in Northeastern Siberia R. Wang et al.
75. Global patterns and key drivers of stream nitrogen concentration: A machine learning approach R. Sheikholeslami & J. Hall
76. Multi-model integration framework for monthly runoff prediction based on variational mode decomposition (VMD) and trend-based modeling S. Wang et al.
77. Optimizing runoff simulation in three mid-high latitude catchments by integrating terrestrial ecosystem modelling, hybrid machine learning, and causal inference H. Zhou et al.
78. Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area X. Gao et al.
79. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn
80. Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin S. Talukdar et al.
81. Battery aging mode identification across NMC compositions and designs using machine learning B. Chen et al.
82. Assessing influential rainfall–runoff variables to simulate daily streamflow using random forest F. Vilaseca et al.
83. A Bio-Inspired Artificial Intelligence Framework Leveraging Remote Sensing for Groundwater Storage Modeling in Climate-Stressed Regions A. Elmotawakkil et al.
84. Enhancing monthly precipitation forecasting by integrating multi-source data with machine learning models: a study in the Upper Blue Nile Basin J. Mohammed et al.
85. A novel approach for prediction of daily streamflow discharge data using correlation based feature selection and random forest method L. LATİFOĞLU
86. On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran E. Merufinia et al.
87. The physically based, ML and hybrid models for runoff modeling in data-scarce basins S. Khanal & M. Kafle
88. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al.
89. Integrated Sediment Yield Estimation and Control in Erosion-Prone Watersheds: A Systematic Review of Models, Strategies, and Emerging Technologies K. Robles et al.
90. Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (EEMD) based data-driven models in the Euphrates sub-basin, Turkey C. Sezen
91. A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models K. Robles et al.
92. Enhancing monthly streamflow forecasting for Brazilian hydropower plants through climate index integration with stochastic methods T. Lappicy & C. Lima
93. Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting J. Fang et al.
94. Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter R. Palomaki & E. Sproles
95. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al.
96. Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha
97. Modeling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application To the zhaiyk-caspian basin, Kazakhstan S. Alimkulov et al.
98. Coherent changes in global high and low flows Q. Huang et al.
99. Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al.
100. Machine Learning-Based Reconstruction and Prediction of Groundwater Time Series in the Allertal, Germany T. Tran et al.
101. What drives the distinct evolution of the Aral Sea and Lake Balkhash? Insights from a novel CD-RF-FA method S. Liu et al.