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A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful...

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Main Authors: Hayder G., Solihin M.I., Kushiar K.F.B.
Other Authors: 56239664100
Format: Article
Published: Polish Society of Ecological Engineering (PTIE) 2023
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spelling my.uniten.dspace-265212023-05-29T17:11:28Z A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems Hayder G. Solihin M.I. Kushiar K.F.B. 56239664100 16644075500 57212462702 Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems. � 2021, Journal of Ecological Engineering. All Rights Reserved. Final 2023-05-29T09:11:28Z 2023-05-29T09:11:28Z 2021 Article 10.12911/22998993/137847 2-s2.0-85110556488 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110556488&doi=10.12911%2f22998993%2f137847&partnerID=40&md5=92759d91d61d0387b84a98c3a1ab1748 https://irepository.uniten.edu.my/handle/123456789/26521 22 7 20 27 All Open Access, Gold Polish Society of Ecological Engineering (PTIE) 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 Sediment is a universal issue that is generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems. � 2021, Journal of Ecological Engineering. All Rights Reserved.
author2 56239664100
author_facet 56239664100
Hayder G.
Solihin M.I.
Kushiar K.F.B.
format Article
author Hayder G.
Solihin M.I.
Kushiar K.F.B.
spellingShingle Hayder G.
Solihin M.I.
Kushiar K.F.B.
A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
author_sort Hayder G.
title A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
title_short A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
title_full A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
title_fullStr A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
title_full_unstemmed A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems
title_sort performance comparison of various artificial intelligence approaches for estimation of sediment of river systems
publisher Polish Society of Ecological Engineering (PTIE)
publishDate 2023
_version_ 1806425926936821760
score 13.222552

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