51 citations as recorded by crossref.

1. 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
2. Comparative Study of Multi-Combination Models for Medium- and Long-Term Runoff Prediction in Weihe River H. Liu et al. 10.1109/ACCESS.2023.3312185
3. Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States Z. Xu et al. 10.3390/w11020228
4. Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system A. Maghsoodi 10.1016/j.omega.2022.102787
5. Multi-phase hybrid bidirectional deep learning model integrated with Markov chain Monte Carlo bivariate copulas function for streamflow prediction A. Iqbal & T. Siddiqi 10.1007/s00477-023-02632-9
6. Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds L. Pham et al. 10.5194/hess-25-2997-2021
7. Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series S. Kim et al. 10.3390/su12229720
8. A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting Á. Ossandón et al. 10.1029/2021WR029920
9. Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach H. Aguilera et al. 10.1080/02626667.2019.1651933
10. Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost R. Szczepanek 10.3390/hydrology9120226
11. Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset S. Saravanan et al. 10.3390/su151612295
12. Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization I. Kenny 10.1093/comjnl/bxz106
13. Coupling the Xinanjiang model and wavelet-based random forests method for improved daily streamflow simulation J. Wang et al. 10.2166/hydro.2021.111
14. Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections D. Woodson et al. 10.1029/2021WR030936
15. COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği N. ÖZEN et al. 10.31590/ejosat.855113
16. A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia) J. Quilty & J. Adamowski 10.1016/j.envsoft.2021.105119
17. Drought forecasting using the Prophet model in a semi-arid climate region of western India A. Basak et al. 10.1080/02626667.2022.2082876
18. The R Language: An Engine for Bioinformatics and Data Science F. Giorgi et al. 10.3390/life12050648
19. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models G. Papacharalampous et al. 10.1016/j.advwatres.2019.103471
20. Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale G. Papacharalampous et al. 10.1016/j.advwatres.2019.103470
21. Near-real-time satellite precipitation data ingestion into peak runoff forecasting models P. Muñoz et al. 10.1016/j.envsoft.2022.105582
22. Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. 10.1016/j.jhydrol.2019.123957
23. Long-Lead Forecasting of Runoff Season Flows in the Colorado River Basin Using a Random Forest Approach D. Woodson et al. 10.1061/JWRMD5.WRENG-6167
24. Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh S. Shahriar et al. 10.1007/s11869-020-00878-8
25. Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia N. Zhao et al. 10.1371/journal.pntd.0008056
26. A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction M. Abbasi et al. 10.1016/j.jhydrol.2020.125717
27. Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. 10.1029/2022WR033194
28. A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
29. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble D. Althoff et al. 10.1007/s00477-021-01980-8
30. Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment P. Contreras et al. 10.3390/atmos12020238
31. Short-term water demand forecasting using data-centric machine learning approaches G. Liu et al. 10.2166/hydro.2023.163
32. 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
33. Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow G. Reis et al. 10.1016/j.jenvman.2021.112625
34. Applicability of machine learning techniques for multi-time step ahead runoff forecasting T. Bajirao et al. 10.1007/s11600-022-00749-z
35. Long-Term Ampacity Prediction Method for Cable Intermediate Joints Based on the Prophet Model Z. Zhang et al. 10.3390/pr12040748
36. Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks X. Shu et al. 10.1007/s11269-022-03165-6
37. Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil T. Nunes Carvalho et al. 10.1061/(ASCE)WR.1943-5452.0001310
38. Explaining the Flood Behavior for the Bridge Collapse Sites F. Ashraf et al. 10.3390/jmse10091241
39. A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes J. Quilty & J. Adamowski 10.1016/j.envsoft.2020.104718
40. Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant? J. Devaraj et al. 10.1016/j.rinp.2021.103817
41. A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources H. Tyralis et al. 10.3390/w11050910
42. 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
43. Estimating annual runoff in response to forest change: A statistical method based on random forest M. Li et al. 10.1016/j.jhydrol.2020.125168
44. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models Y. Wang et al. 10.1186/s12879-022-07472-6
45. Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
46. Daily Flow Modeling With Random Forest and K-Nearest Neighbor Methods H. DALKILIÇ et al. 10.18185/erzifbed.949126
47. Daily scale streamflow forecasting in multiple stream orders of Cauvery River, India: Application of advanced ensemble and deep learning models S. Naganna et al. 10.1016/j.jhydrol.2023.130320
48. Deriving transmission losses in ephemeral rivers using satellite imagery and machine learning A. Di Ciacca et al. 10.5194/hess-27-703-2023
49. Long-term forecasting system using wavelet – nonlinear autoregressive neural network conjunction model R. Kummong & S. Supratid 10.1108/JM2-11-2018-0184
50. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. 10.1016/j.jhydrol.2019.04.070
51. Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models M. Zolfaghari & M. Golabi 10.1016/j.renene.2021.02.017