Prediction of suspended sediment distributions using data mining algorithms

Distribution of sediment concentration in open-channel flows, particularly in rivers, is one of the most important factors in understanding the river behavior, water quality, and design of hydraulic structures. Therefore, to determine the amount of transported suspended sediment, the sediment concen...

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Autores principales:	Yaser Mehri, Mohsen Nasrabadi, Mohammad Hossein Omid
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Sediment concentration distribution Intelligent methods Open channel flows Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/bd06429d0cdd40f2a52aeecabf777532
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spelling	oai:doaj.org-article:bd06429d0cdd40f2a52aeecabf7775322021-11-22T04:21:37ZPrediction of suspended sediment distributions using data mining algorithms2090-447910.1016/j.asej.2021.02.034https://doaj.org/article/bd06429d0cdd40f2a52aeecabf7775322021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001696https://doaj.org/toc/2090-4479Distribution of sediment concentration in open-channel flows, particularly in rivers, is one of the most important factors in understanding the river behavior, water quality, and design of hydraulic structures. Therefore, to determine the amount of transported suspended sediment, the sediment concentration distribution must be measured with high accuracy. In the present study, four intelligent methods of ANFIS-PSO, ANFIS-GA, ANFIS, and GMDH were used to predict the sediment concentration distribution. Since both GA and PSO optimization methods were used to optimize the ANFIS model, the performance of these models was significantly improved and their accuracies were increased. The results showed that the methods of ANFIS-PSO, ANFIS-GA, ANFIS, and GMDH were, respectively, the most accurate methods for prediction of suspended sediment distribution. Based on the evaluation of these methods, it was concluded that intelligent methods have considerable accuracy in predicting parameters affecting the suspended sediment distribution. Accordingly, considering the performance of these methods, a combination of optimization and intelligent methods may be useful for predicting sediment concentration distribution. It was also found that the ANFIS-PSO method can be a more appropriate and accurate method than other methods.Yaser MehriMohsen NasrabadiMohammad Hossein OmidElsevierarticleSediment concentration distributionIntelligent methodsOpen channel flowsEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3439-3450 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sediment concentration distribution Intelligent methods Open channel flows Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Sediment concentration distribution Intelligent methods Open channel flows Engineering (General). Civil engineering (General) TA1-2040 Yaser Mehri Mohsen Nasrabadi Mohammad Hossein Omid Prediction of suspended sediment distributions using data mining algorithms
description	Distribution of sediment concentration in open-channel flows, particularly in rivers, is one of the most important factors in understanding the river behavior, water quality, and design of hydraulic structures. Therefore, to determine the amount of transported suspended sediment, the sediment concentration distribution must be measured with high accuracy. In the present study, four intelligent methods of ANFIS-PSO, ANFIS-GA, ANFIS, and GMDH were used to predict the sediment concentration distribution. Since both GA and PSO optimization methods were used to optimize the ANFIS model, the performance of these models was significantly improved and their accuracies were increased. The results showed that the methods of ANFIS-PSO, ANFIS-GA, ANFIS, and GMDH were, respectively, the most accurate methods for prediction of suspended sediment distribution. Based on the evaluation of these methods, it was concluded that intelligent methods have considerable accuracy in predicting parameters affecting the suspended sediment distribution. Accordingly, considering the performance of these methods, a combination of optimization and intelligent methods may be useful for predicting sediment concentration distribution. It was also found that the ANFIS-PSO method can be a more appropriate and accurate method than other methods.
format	article
author	Yaser Mehri Mohsen Nasrabadi Mohammad Hossein Omid
author_facet	Yaser Mehri Mohsen Nasrabadi Mohammad Hossein Omid
author_sort	Yaser Mehri
title	Prediction of suspended sediment distributions using data mining algorithms
title_short	Prediction of suspended sediment distributions using data mining algorithms
title_full	Prediction of suspended sediment distributions using data mining algorithms
title_fullStr	Prediction of suspended sediment distributions using data mining algorithms
title_full_unstemmed	Prediction of suspended sediment distributions using data mining algorithms
title_sort	prediction of suspended sediment distributions using data mining algorithms
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/bd06429d0cdd40f2a52aeecabf777532
work_keys_str_mv	AT yasermehri predictionofsuspendedsedimentdistributionsusingdataminingalgorithms AT mohsennasrabadi predictionofsuspendedsedimentdistributionsusingdataminingalgorithms AT mohammadhosseinomid predictionofsuspendedsedimentdistributionsusingdataminingalgorithms
_version_	1718418245488214016

Prediction of suspended sediment distributions using data mining algorithms

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