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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2022.2073565 |
| _version_ | 1818474642254331904 |
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| author | Nouar AlDahoul Ali Najah Ahmed Mohammed Falah Allawi Mohsen Sherif Ahmed Sefelnasr Kwok-wing Chau Ahmed El-Shafie |
| author_facet | Nouar AlDahoul Ali Najah Ahmed Mohammed Falah Allawi Mohsen Sherif Ahmed Sefelnasr Kwok-wing Chau Ahmed El-Shafie |
| author_sort | Nouar AlDahoul |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-14T04:39:13Z |
| format | Article |
| id | doaj.art-98c23cf9711246b08c9195214b27390c |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-04-14T04:39:13Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-98c23cf9711246b08c9195214b27390c2022-12-22T02:11:44ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2022-12-011611211123210.1080/19942060.2022.2073565A comparison of machine learning models for suspended sediment load classificationNouar AlDahoul0Ali Najah Ahmed1Mohammed Falah Allawi2Mohsen Sherif3Ahmed Sefelnasr4Kwok-wing Chau5Ahmed El-Shafie6Faculty of Engineering, Multimedia University, Cyberjaya, MalaysiaDepartment of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang, MalaysiaDams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi, IraqCivil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesNational Water and Energy Center, United Arab Emirates University, Al Ain, United Arab EmiratesDepartment of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Hong KongDepartment of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, MalaysiaThe 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.https://www.tandfonline.com/doi/10.1080/19942060.2022.2073565Suspended sediment loadextreme gradient boostingrandom forestsupport vector machinemulti-layer perceptronk-nearest neighbor |
| spellingShingle | Nouar AlDahoul Ali Najah Ahmed Mohammed Falah Allawi Mohsen Sherif Ahmed Sefelnasr Kwok-wing Chau Ahmed El-Shafie A comparison of machine learning models for suspended sediment load classification Engineering Applications of Computational Fluid Mechanics Suspended sediment load extreme gradient boosting random forest support vector machine multi-layer perceptron k-nearest neighbor |
| title | 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_short | A comparison of machine learning models for suspended sediment load classification |
| title_sort | comparison of machine learning models for suspended sediment load classification |
| topic | Suspended sediment load extreme gradient boosting random forest support vector machine multi-layer perceptron k-nearest neighbor |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2022.2073565 |
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