Staff View: A comparison of machine learning models for suspended sediment load classification

A comparison of machine learning models for suspended sediment load classification

The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and c...

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Main Authors:	AlDahoul N., Ahmed A.N., Allawi M.F., Sherif M., Sefelnasr A., Chau K.-W., El-Shafie A.
Other Authors:	56656478800
Format:	Article
Published:	Taylor and Francis Ltd. 2023
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id	my.uniten.dspace-27190
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spelling	my.uniten.dspace-271902023-05-29T17:40:44Z A comparison of machine learning models for suspended sediment load classification AlDahoul N. Ahmed A.N. Allawi M.F. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. 56656478800 57214837520 57057678400 7005414714 6505592467 7202674661 16068189400 The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Final 2023-05-29T09:40:44Z 2023-05-29T09:40:44Z 2022 Article 10.1080/19942060.2022.2073565 2-s2.0-85131082973 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131082973&doi=10.1080%2f19942060.2022.2073565&partnerID=40&md5=12d87d57d37a067c1c7cd87ac65c01b8 https://irepository.uniten.edu.my/handle/123456789/27190 16 1 1211 1232 All Open Access, Gold Taylor and Francis Ltd. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	The suspended sediment load (SSL) is one of the major hydrological processes affecting the sustainability of river planning and management. Moreover, sediments have a significant impact on dam operation and reservoir capacity. To this end, reliable and applicable models are required to compute and classify the SSL in rivers. The application of machine learning models has become common to solve complex problems such as SSL modeling. The present research investigated the ability of several models to classify the SSL data. This investigation aims to explore a new version of machine learning classifiers for SSL classification at Johor River, Malaysia. Extreme gradient boosting, random forest, support vector machine, multi-layer perceptron and k-nearest neighbors classifiers have been used to classify the SSL data. The sediment values are divided into multiple discrete ranges, where each range can be considered as one category or class. This study illustrates two different scenarios related to the number of categories, which are five and 10 categories, with two time scales, daily and weekly. The performance of the proposed models was evaluated by several statistical indicators. Overall, the proposed models achieved excellent classification of the SSL data under various scenarios. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
author2	56656478800
author_facet	56656478800 AlDahoul N. Ahmed A.N. Allawi M.F. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A.
format	Article
author	AlDahoul N. Ahmed A.N. Allawi M.F. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A.
spellingShingle	AlDahoul N. Ahmed A.N. Allawi M.F. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. A comparison of machine learning models for suspended sediment load classification
author_sort	AlDahoul N.
title	A comparison of machine learning models for suspended sediment load classification
title_short	A comparison of machine learning models for suspended sediment load classification
title_full	A comparison of machine learning models for suspended sediment load classification
title_fullStr	A comparison of machine learning models for suspended sediment load classification
title_full_unstemmed	A comparison of machine learning models for suspended sediment load classification
title_sort	comparison of machine learning models for suspended sediment load classification
publisher	Taylor and Francis Ltd.
publishDate	2023
_version_	1806427657731047424
score	13.222552

A comparison of machine learning models for suspended sediment load classification

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