Streamflow forecasting
Author:
Publisher
Elsevier
Reference325 articles.
1. 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;Abbasi;J. Hydrol.,2020
2. Development of a coupled wavelet transform and evolutionary Levenberg-Marquardt neural networks for hydrological process modeling;Abbaszadeh;Comput. Intell.,2017
3. Wavelet-copula-based mutual information for rainfall forecasting applications;Abdourahamane;Hydrol. Process.,2019
4. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments;Abrahart;Hydrol. Process.,2000
5. Application of partial least-squares regression in seasonal streamflow forecasting;Abudu;J. Hydrol. Eng.,2010
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1. Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil;Hydrology;2023-11-08
2. Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective;Water Science & Technology;2023-10-25
3. A review of hybrid deep learning applications for streamflow forecasting;Journal of Hydrology;2023-10
4. Modeling streamflow in Sot river catchment of Uttar Pradesh, India;Arabian Journal of Geosciences;2023-09-16
5. Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset;Sustainability;2023-08-11
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