70 citations as recorded by crossref.

1. Modelling of snow and glacier melt dynamics in a mountainous river basin using integrated SWAT and machine learning approaches A. Gogineni et al. 10.1007/s12145-024-01397-1
2. A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting M. Danesh et al. 10.1007/s11269-024-04052-y
3. Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin S. Tang et al. 10.3390/rs13234831
4. Exploitation of the ensemble-based machine learning strategies to elevate the precision of CORDEX regional simulations in precipitation projection A. Ghaemi et al. 10.1007/s12145-024-01234-5
5. 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. 10.1134/S1064229324600477
6. Modeling riparian flood plain wetland water richness in pursuance of damming and linking it with a methane emission rate S. Pal & R. Sarda 10.1080/10106049.2021.1988726
7. Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States B. Gaertner 10.1016/j.scitotenv.2023.169211
8. Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique W. Achini Ishankha et al. 10.1016/j.jenvman.2024.123082
9. Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin R. Noor et al. 10.3390/rs15020318
10. Applicability of machine learning techniques for multi-time step ahead runoff forecasting T. Bajirao et al. 10.1007/s11600-022-00749-z
11. Machine learning-based prediction of scour depth evolution around spur dikes R. Tabassum et al. 10.2166/hydro.2024.160
12. Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin G. Özdoğan-Sarıkoç & F. Dadaser-Celik 10.1007/s11356-024-33732-w
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 10.2166/wst.2023.330
14. Comparison and integration of hydrological models and machine learning models in global monthly streamflow simulation J. Zhang et al. 10.1016/j.jhydrol.2024.132549
15. A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting C. Xu et al. 10.1016/j.jhydrol.2024.131494
16. A predictive model of velocity for local hydrokinetic power assessment based on remote sensing data A. MacMillan et al. 10.1016/j.renene.2023.04.146
17. A random forest approach to improve estimates of tributary nutrient loading P. Isles 10.1016/j.watres.2023.120876
18. Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data Z. Hajibagheri et al. 10.1007/s11356-024-35482-1
19. Assessing the influencing factors of soil susceptibility to wind erosion: A wind tunnel experiment with a machine learning and model-agnostic interpretation approach Y. Zhao et al. 10.1016/j.catena.2022.106324
20. Role of Aerosols in Spring Blooms in the Central Yellow Sea During the COVID-19 Lockdown by China J. Baek et al. 10.3389/fmars.2022.911819
21. Appraisal of data-driven techniques for predicting short-term streamflow in tropical catchment K. Yeoh et al. 10.2166/wst.2023.193
22. Multi-Variables-Driven Model Based on Random Forest and Gaussian Process Regression for Monthly Streamflow Forecasting N. Sun et al. 10.3390/w14111828
23. Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics O. Jaffar et al. 10.1016/j.ejrh.2024.101899
24. Microstructure and mechanical properties evaluation of automotive plate steel based on micromagnetic NDT technologies H. Sheng et al. 10.1016/j.measurement.2022.111459
25. Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al. 10.1007/s11356-024-33594-2
26. Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials M. Ahmad et al. 10.1155/2022/9415863
27. A Novel and Non-Invasive Approach to Evaluating Soil Moisture without Soil Disturbances: Contactless Ultrasonic System D. Woo et al. 10.3390/s22197450
28. Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. 10.1080/02626667.2024.2426720
29. Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights V. Kartal et al. 10.1007/s11069-024-07043-9
30. Genetic algorithm-assisted an improved AdaBoost double-layer for oil temperature prediction of TBM J. Ren et al. 10.1016/j.aei.2022.101563
31. An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting W. Fang et al. 10.1038/s41598-024-81502-y
32. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
33. Global patterns and key drivers of stream nitrogen concentration: A machine learning approach R. Sheikholeslami & J. Hall 10.1016/j.scitotenv.2023.161623
34. Coupling and Comparison of Physical Mechanism and Machine Learning Models for Water Level Simulation in Plain River Network Area X. Gao et al. 10.3390/app142412008
35. Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
36. Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin S. Talukdar et al. 10.1007/s10661-022-10696-3
37. Battery aging mode identification across NMC compositions and designs using machine learning B. Chen et al. 10.1016/j.joule.2022.10.016
38. Assessing influential rainfall–runoff variables to simulate daily streamflow using random forest F. Vilaseca et al. 10.1080/02626667.2023.2232356
39. 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. 10.1007/s40808-024-02223-9
40. Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
41. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. 10.1016/j.eswa.2024.126004
42. A novel approach for prediction of daily streamflow discharge data using correlation based feature selection and random forest method L. LATİFOĞLU 10.35860/iarej.987245
43. Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research S. Doğanalp & İ. Köz 10.3390/atmos15080986
44. Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing K. Zhang et al. 10.1029/2022WR034092
45. On the simulation of streamflow using hybrid tree-based machine learning models: a case study of Kurkursar basin, Iran E. Merufinia et al. 10.1007/s12517-022-11045-x
46. Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments H. Mushtaq et al. 10.1007/s00704-024-04932-8
47. Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (EEMD) based data-driven models in the Euphrates sub-basin, Turkey C. Sezen 10.1007/s12145-023-01078-5
48. Enhancing monthly streamflow forecasting for Brazilian hydropower plants through climate index integration with stochastic methods T. Lappicy & C. Lima 10.1590/2318-0331.282320230118
49. Streamflow Prediction with Time-Lag-Informed Random Forest and Its Performance Compared to SWAT in Diverse Catchments D. Moges et al. 10.3390/w16192805
50. Assessing machine learning models for streamflow estimation: a case study in Oued Sebaou watershed (Northern Algeria) Z. Abda et al. 10.1080/02626667.2022.2083511
51. Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting J. Fang et al. 10.1016/j.jhydrol.2024.131275
52. 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. 10.1016/j.atmosres.2022.106159
53. Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter R. Palomaki & E. Sproles 10.3390/rs14225644
54. Machine learning approaches for reconstructing gridded precipitation based on multiple source products G. Nguyen et al. 10.1016/j.ejrh.2023.101475
55. Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems G. Kopsiaftis et al. 10.3389/frwa.2023.1195029
56. Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques S. Velivelli et al. 10.1007/s00704-024-05146-8
57. Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 2011–2020 in Yunnan, China Z. Zhu et al. 10.1109/JSTARS.2024.3507853
58. Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al. 10.3390/w15030427
59. Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting J. Liu et al. 10.1007/s12145-024-01569-z
60. Conjunct application of machine learning and game theory in groundwater quality mapping A. Khiavi et al. 10.1007/s12665-023-11059-y
61. What drives the distinct evolution of the Aral Sea and Lake Balkhash? Insights from a novel CD-RF-FA method S. Liu et al. 10.1016/j.ejrh.2024.102014
62. Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections D. Woodson et al. 10.1029/2021WR030936
63. Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis F. Ahmadi et al. 10.1007/s00477-021-02159-x
64. Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir Y. Lu et al. 10.3390/w12113231
65. Evaluating different machine learning algorithms for snow water equivalent prediction M. Vafakhah et al. 10.1007/s12145-022-00846-z
66. A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression J. Ma et al. 10.1007/s00477-022-02183-5
67. Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques C. Ikeagwuani & D. Nwonu 10.1007/s41062-022-00875-z
68. A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows A. Al-Juboori 10.1007/s11269-020-02757-4
69. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble D. Althoff et al. 10.1007/s00477-021-01980-8
70. Prediction of CSG splitting tensile strength based on XGBoost-RF model L. Guo et al. 10.1016/j.mtcomm.2023.105350