A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers

Suspended sediment load (SSL) estimation is essential for both short- and long-term water resources management. Suspended sediments are taken into account as an important factor of the service life of hydraulic structures such as dams. The aim of this research is to estimat SSL by coupling intrinsic...

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Autores principales:	Nannan Zhao, Alireza Ghaemi, Chengwen Wu, Shahab S. Band, Kwok-Wing Chau, Atef Zaguia, Majdi Mafarja, Amir H. Mosavi
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	suspended sediment load machine learning artificial intelligence intrinsic time-scale decomposition technique evolutionary polynomial regression Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/3506676583014db5aef9c27646b382d3
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spelling	oai:doaj.org-article:3506676583014db5aef9c27646b382d32021-11-26T11:19:48ZA decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers1994-20601997-003X10.1080/19942060.2021.1990133https://doaj.org/article/3506676583014db5aef9c27646b382d32021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/19942060.2021.1990133https://doaj.org/toc/1994-2060https://doaj.org/toc/1997-003XSuspended sediment load (SSL) estimation is essential for both short- and long-term water resources management. Suspended sediments are taken into account as an important factor of the service life of hydraulic structures such as dams. The aim of this research is to estimat SSL by coupling intrinsic time-scale decomposition (ITD) and two kinds of DDM, namely evolutionary polynomial regression (EPR) and model tree (MT) DDMs, at the Sarighamish and Varand Stations in Iran. Measured data based on their lag times are decomposed into several proper rotation components (PRCs) and a residual, which are then considered as inputs for the proposed model. Results indicate that the prediction accuracy of ITD-EPR is the best for both the Sarighamish (R2 = 0.92 and WI = 0.96) and Varand (R2 = 0.92 and WI = 0.93) Stations (WI is the Willmott index of agreement), while a standalone MT model performs poorly for these stations compared with other approaches (EPR, ITD-EPR and ITD-MT) although peak SSL values are approximately equal to those by ITD-EPR. Results of the proposed models are also compared with those of the sediment rating curve (SRC) method. The ITD-EPR predictions are remarkably superior to those by the SRC method with respect to several conventional performance evaluation metrics.Nannan ZhaoAlireza GhaemiChengwen WuShahab S. BandKwok-Wing ChauAtef ZaguiaMajdi MafarjaAmir H. MosaviTaylor & Francis Grouparticlesuspended sediment loadmachine learningartificial intelligenceintrinsic time-scale decomposition techniqueevolutionary polynomial regressionEngineering (General). Civil engineering (General)TA1-2040ENEngineering Applications of Computational Fluid Mechanics, Vol 15, Iss 1, Pp 1811-1829 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	suspended sediment load machine learning artificial intelligence intrinsic time-scale decomposition technique evolutionary polynomial regression Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	suspended sediment load machine learning artificial intelligence intrinsic time-scale decomposition technique evolutionary polynomial regression Engineering (General). Civil engineering (General) TA1-2040 Nannan Zhao Alireza Ghaemi Chengwen Wu Shahab S. Band Kwok-Wing Chau Atef Zaguia Majdi Mafarja Amir H. Mosavi A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
description	Suspended sediment load (SSL) estimation is essential for both short- and long-term water resources management. Suspended sediments are taken into account as an important factor of the service life of hydraulic structures such as dams. The aim of this research is to estimat SSL by coupling intrinsic time-scale decomposition (ITD) and two kinds of DDM, namely evolutionary polynomial regression (EPR) and model tree (MT) DDMs, at the Sarighamish and Varand Stations in Iran. Measured data based on their lag times are decomposed into several proper rotation components (PRCs) and a residual, which are then considered as inputs for the proposed model. Results indicate that the prediction accuracy of ITD-EPR is the best for both the Sarighamish (R2 = 0.92 and WI = 0.96) and Varand (R2 = 0.92 and WI = 0.93) Stations (WI is the Willmott index of agreement), while a standalone MT model performs poorly for these stations compared with other approaches (EPR, ITD-EPR and ITD-MT) although peak SSL values are approximately equal to those by ITD-EPR. Results of the proposed models are also compared with those of the sediment rating curve (SRC) method. The ITD-EPR predictions are remarkably superior to those by the SRC method with respect to several conventional performance evaluation metrics.
format	article
author	Nannan Zhao Alireza Ghaemi Chengwen Wu Shahab S. Band Kwok-Wing Chau Atef Zaguia Majdi Mafarja Amir H. Mosavi
author_facet	Nannan Zhao Alireza Ghaemi Chengwen Wu Shahab S. Band Kwok-Wing Chau Atef Zaguia Majdi Mafarja Amir H. Mosavi
author_sort	Nannan Zhao
title	A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
title_short	A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
title_full	A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
title_fullStr	A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
title_full_unstemmed	A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
title_sort	decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/3506676583014db5aef9c27646b382d3
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A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers

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