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Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

article; artificial neural network; deep learning; Malaysia; particle resuspension; prediction; reliability; river; short term memory; support vector machine

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Bibliographic Details
Main Authors: Essam Y., Huang Y.F., Birima A.H., Ahmed A.N., El-Shafie A.
Other Authors: 57203146903
Format: Article
Published: Nature Research 2023
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spelling my.uniten.dspace-266632023-05-29T17:36:06Z Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms Essam Y. Huang Y.F. Birima A.H. Ahmed A.N. El-Shafie A. 57203146903 55807263900 23466519000 57214837520 16068189400 article; artificial neural network; deep learning; Malaysia; particle resuspension; prediction; reliability; river; short term memory; support vector machine High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia. � 2022, The Author(s). Final 2023-05-29T09:36:06Z 2023-05-29T09:36:06Z 2022 Article 10.1038/s41598-021-04419-w 2-s2.0-85122651674 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122651674&doi=10.1038%2fs41598-021-04419-w&partnerID=40&md5=6632338cb0a65d32db5acb931cd78025 https://irepository.uniten.edu.my/handle/123456789/26663 12 1 302 All Open Access, Gold, Green Nature Research 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 article; artificial neural network; deep learning; Malaysia; particle resuspension; prediction; reliability; river; short term memory; support vector machine
author2 57203146903
author_facet 57203146903
Essam Y.
Huang Y.F.
Birima A.H.
Ahmed A.N.
El-Shafie A.
format Article
author Essam Y.
Huang Y.F.
Birima A.H.
Ahmed A.N.
El-Shafie A.
spellingShingle Essam Y.
Huang Y.F.
Birima A.H.
Ahmed A.N.
El-Shafie A.
Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
author_sort Essam Y.
title Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
title_short Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
title_full Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
title_fullStr Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
title_full_unstemmed Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms
title_sort predicting suspended sediment load in peninsular malaysia using support vector machine and deep learning algorithms
publisher Nature Research
publishDate 2023
_version_ 1806426044926787584
score 13.214268

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