Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow

Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow

River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine lea...

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Bibliographic Details
Main Authors: Kegang Wang, Shahab S. Band, Rasoul Ameri, Meghdad Biyari, Tao Hai, Chung-Chian Hsu, Myriam Hadjouni, Hela Elmannai, Kwok-Wing Chau, Amir Mosavi
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
River streamflow
wavelet
machine learning
hybrid models
estimation
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2022.2119281

Internet

https://www.tandfonline.com/doi/10.1080/19942060.2022.2119281

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