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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

algorithm; Malaysia; river; support vector machine; Algorithms; Deep Learning; Malaysia; Rivers; Support Vector Machine

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Bibliographic Details
Main Authors: Essam Y., Huang Y.F., Ng J.L., Birima A.H., Ahmed A.N., El-Shafie A.
Other Authors: 57203146903
Format: Article
Published: Nature Research 2023
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id my.uniten.dspace-26661
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spelling my.uniten.dspace-266612023-05-29T17:36:05Z Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms Essam Y. Huang Y.F. Ng J.L. Birima A.H. Ahmed A.N. El-Shafie A. 57203146903 55807263900 57192698412 23466519000 57214837520 16068189400 algorithm; Malaysia; river; support vector machine; Algorithms; Deep Learning; Malaysia; Rivers; Support Vector Machine Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), artificial neural network (ANN), and long short-term memory (LSTM), are tested and developed to predict SF for 11 different rivers throughout Peninsular Malaysia. SF data sets for the rivers were collected from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a universal model that is most capable of predicting SFs for rivers within Peninsular Malaysia. Based on the findings, the ANN3 model which was developed using the ANN algorithm and input scenario 3 (inputs consisting of previous 3�days SF) is deduced as the best overall ML model for SF prediction as it outperformed all the other models in 4 out of 11 of the tested data sets; and obtained among the highest average RMs with a score of 3.27, hence indicating that the model is very adaptable and reliable in accurately predicting SF based on different data sets and river case studies. Therefore, the ANN3 model is proposed as a universal model for SF prediction within Peninsular Malaysia. � 2022, The Author(s). Final 2023-05-29T09:36:05Z 2023-05-29T09:36:05Z 2022 Article 10.1038/s41598-022-07693-4 2-s2.0-85126218963 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126218963&doi=10.1038%2fs41598-022-07693-4&partnerID=40&md5=4157393c5cd5160aff4b650599dcfd58 https://irepository.uniten.edu.my/handle/123456789/26661 12 1 3883 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 algorithm; Malaysia; river; support vector machine; Algorithms; Deep Learning; Malaysia; Rivers; Support Vector Machine
author2 57203146903
author_facet 57203146903
Essam Y.
Huang Y.F.
Ng J.L.
Birima A.H.
Ahmed A.N.
El-Shafie A.
format Article
author Essam Y.
Huang Y.F.
Ng J.L.
Birima A.H.
Ahmed A.N.
El-Shafie A.
spellingShingle Essam Y.
Huang Y.F.
Ng J.L.
Birima A.H.
Ahmed A.N.
El-Shafie A.
Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
author_sort Essam Y.
title Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
title_short Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
title_full Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
title_fullStr Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
title_full_unstemmed Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
title_sort predicting streamflow in peninsular malaysia using support vector machine and deep learning algorithms
publisher Nature Research
publishDate 2023
_version_ 1806423300389208064
score 13.214268

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