49 citations as recorded by crossref.

1. M-EDEM: A MNN-based Empirical Decomposition Ensemble Method for improved time series forecasting X. Cai & D. Li 10.1016/j.knosys.2023.111157
2. Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN–SE–BiLSTM model H. Li et al. 10.1038/s41598-023-46264-z
3. Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework A. Jin et al. 10.1061/JHYEFF.HEENG-6254
4. A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
5. Coupling machine learning and physical modelling for predicting runoff at catchment scale S. Zubelzu et al. 10.1016/j.jenvman.2024.120404
6. Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting Y. Lian et al. 10.1007/s11269-021-03002-2
7. Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
8. Closure to the discussion of Ebtehaj et al. on “Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach” S. Mehdizadeh et al. 10.1016/j.jhydrol.2021.126459
9. Can sampling techniques improve the performance of decomposition-based hydrological prediction models? Exploration of some comparative experiments M. He et al. 10.1007/s13201-022-01696-2
10. A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
11. Hydrological peaks evaluation at Chitral and Tarbela stations, Pakistan, using combined Bayesian regularized neural network and signal difference average based variational mode decomposition method: A case study M. Khan & S. Hassan 10.2166/ws.2021.347
12. A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning S. Chen et al. 10.1007/s11356-024-35528-4
13. Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory Y. Lian et al. 10.1007/s11269-022-03097-1
14. An entropy and copula-based framework for streamflow prediction and spatio-temporal identification of drought X. Ju et al. 10.1007/s00477-023-02388-2
15. A novel optimization rainfall coupling model based on stepwise decomposition technique Z. Zheng et al. 10.1038/s41598-024-66663-0
16. Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. 10.1016/j.jhydrol.2022.127936
17. Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network L. Li et al. 10.1007/s11629-021-6824-1
18. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. 10.1109/ACCESS.2023.3318739
19. Regional characteristics’ impact on the performances of the gated recurrent unit on streamflow forecasting Q. Wang et al. 10.2166/ws.2022.041
20. River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China H. Chen et al. 10.1016/j.scitotenv.2023.163571
21. Assessing different roles of baseflow and surface runoff for long-term streamflow forecasting in southeastern China H. Chen et al. 10.1080/02626667.2021.1988612
22. Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions S. Wu et al. 10.1016/j.ecoinf.2024.102914
23. A weights combined model for middle and long-term streamflow forecasts and its value to hydropower maximization Y. Guo et al. 10.1016/j.jhydrol.2021.126794
24. A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework Z. Xu et al. 10.3390/w13233390
25. Comparative Performance Assessment of Physical-Based and Data-Driven Machine-Learning Models for Simulating Streamflow: A Case Study in Three Catchments across the US A. Jin et al. 10.1061/JHYEFF.HEENG-6118
26. Optimization of waterproofing and drainage measures for open-pit mines based on seasonal rainfall time series prediction S. Wang et al. 10.1016/j.envsoft.2024.105957
27. A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
28. Combining Standard Artificial Intelligence Models, Pre-Processing Techniques, and Post-Processing Methods to Improve the Accuracy of Monthly Runoff Predictions in Karst-Area Watersheds C. Mo et al. 10.3390/app13010088
29. Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Türkiye O. Katipoğlu & M. Sarıgöl 10.1016/j.jher.2023.07.004
30. Improving BP artificial neural network model to predict the SPI in arid regions: a case study in Northern Shaanxi, China L. Shaoxuan et al. 10.2166/nh.2022.115
31. Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea J. Kwak et al. 10.3390/w12123461
32. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information L. Xu et al. 10.1016/j.jhydrol.2024.131230
33. Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China S. Li et al. 10.2166/wst.2023.162
34. Runoff forecasting model based on variational mode decomposition and artificial neural networks X. Jing et al. 10.3934/mbe.2022076
35. Stepwise decomposition-integration-prediction framework for runoff forecasting considering boundary correction Z. Xu et al. 10.1016/j.scitotenv.2022.158342
36. Combining two-stage decomposition based machine learning methods for annual runoff forecasting S. Chen et al. 10.1016/j.jhydrol.2021.126945
37. Dynamic multiobjective two-stage fuzzy stochastic strategy for optimal water allocation in inter-basin water division under changing environment: A case study of Hanjiang-to-Weihe water division in Shaanxi Province, China H. Zhang et al. 10.1016/j.jclepro.2024.142175
38. Comparative study of rainfall prediction based on different decomposition methods of VMD X. Zhang et al. 10.1038/s41598-023-47416-x
39. Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China H. Yu et al. 10.1080/02626667.2024.2374868
40. Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
41. Daily streamflow forecasting using hybrid long short-term memory model M. Xie et al. 10.1088/1742-6596/2271/1/012019
42. Prediction of Total Nitrogen and Phosphorus in Surface Water by Deep Learning Methods Based on Multi-Scale Feature Extraction M. He et al. 10.3390/w14101643
43. Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets H. Xu et al. 10.1016/j.jhydrol.2022.128122
44. A 500-year annual runoff reconstruction for 14 selected European catchments S. Nasreen et al. 10.5194/essd-14-4035-2022
45. Interpretable CEEMDAN-FE-LSTM-transformer hybrid model for predicting total phosphorus concentrations in surface water J. Yao et al. 10.1016/j.jhydrol.2024.130609
46. Evaluating the Performance of Several Data Preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series W. Wang et al. 10.1007/s11269-024-03806-y
47. Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence S. Garai & R. Paul 10.1016/j.iswa.2023.200202
48. Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China W. Wang et al. 10.3390/w16111589
49. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502