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Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]

Water is important and critical sources of life. Even though some countries enjoy tropical weather year-round with plenty of water resources like Malaysia, they are still facing scarcity issue. Water demand is influenced by various factors such as population, climate change and water utilization. Th...

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Main Authors: Nasaruddin, Norashikin, Zakaria, Shahida Farhan, Ahmad, Afida, Ul-Saufie, Ahmad Zia, Mohamaed Noor, Norazian
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
T Technology (General)
Technological innovations
Online Access:https://ir.uitm.edu.my/id/eprint/56176/1/56176.pdf
https://ir.uitm.edu.my/id/eprint/56176/
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spelling my.uitm.ir.561762022-12-06T00:07:30Z https://ir.uitm.edu.my/id/eprint/56176/ Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.] Nasaruddin, Norashikin Zakaria, Shahida Farhan Ahmad, Afida Ul-Saufie, Ahmad Zia Mohamaed Noor, Norazian T Technology (General) Technological innovations Water is important and critical sources of life. Even though some countries enjoy tropical weather year-round with plenty of water resources like Malaysia, they are still facing scarcity issue. Water demand is influenced by various factors such as population, climate change and water utilization. This study reviews 45 Scopus articles from year 2015 to 2021 on predicting water demand using Machine Learning (ML) methods which include: neural network, random forest, decision tree, and hybrid optimisation models. The summary of ML methods on the evaluation of their performance in water demand prediction is identified by a comprehensive analysis of the literature. The narrative search of the most relevant literature is classified according to method, prediction type, prediction variables and accuracy rate. The review identified several machine learning methods that are commonly used which include decision tree, neural network, random forest and hybrid method. In conclusion, the study reports that the accuracy of the method varies according to types of prediction variables used. 2021 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/56176/1/56176.pdf Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]. (2021) In: e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021), 4-5 August 2021. (Submitted)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic T Technology (General)
Technological innovations
spellingShingle T Technology (General)
Technological innovations
Nasaruddin, Norashikin
Zakaria, Shahida Farhan
Ahmad, Afida
Ul-Saufie, Ahmad Zia
Mohamaed Noor, Norazian
Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
description Water is important and critical sources of life. Even though some countries enjoy tropical weather year-round with plenty of water resources like Malaysia, they are still facing scarcity issue. Water demand is influenced by various factors such as population, climate change and water utilization. This study reviews 45 Scopus articles from year 2015 to 2021 on predicting water demand using Machine Learning (ML) methods which include: neural network, random forest, decision tree, and hybrid optimisation models. The summary of ML methods on the evaluation of their performance in water demand prediction is identified by a comprehensive analysis of the literature. The narrative search of the most relevant literature is classified according to method, prediction type, prediction variables and accuracy rate. The review identified several machine learning methods that are commonly used which include decision tree, neural network, random forest and hybrid method. In conclusion, the study reports that the accuracy of the method varies according to types of prediction variables used.
format Conference or Workshop Item
author Nasaruddin, Norashikin
Zakaria, Shahida Farhan
Ahmad, Afida
Ul-Saufie, Ahmad Zia
Mohamaed Noor, Norazian
author_facet Nasaruddin, Norashikin
Zakaria, Shahida Farhan
Ahmad, Afida
Ul-Saufie, Ahmad Zia
Mohamaed Noor, Norazian
author_sort Nasaruddin, Norashikin
title Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
title_short Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
title_full Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
title_fullStr Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
title_full_unstemmed Water demand prediction using machine learning: a review / Norashikin Nasaruddin ... [et al.]
title_sort water demand prediction using machine learning: a review / norashikin nasaruddin ... [et al.]
publishDate 2021
url https://ir.uitm.edu.my/id/eprint/56176/1/56176.pdf
https://ir.uitm.edu.my/id/eprint/56176/
_version_ 1751539853739163648
score 13.211869

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