The prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models Machine (SVM) models.

Syazwan Nizam, Moni and Edriyana, Abd Aziz and Anwar P. P., Abdul Majeed and Marlinda, Malek (2021) The prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models Machine (SVM) models. Physics and Chemistry of the Earth, 123 (103052). pp. 1-7. ISSN 1474-7065. (Published)

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DOI/Official URL: https://doi.org/10.1016/j.pce.2021.103052

Abstract

The prediction of the blue water footprint in water services such as in water treatment plants (WTPs) is non-trivial to water resource management. Currently, the sustainability of water resources is of great concern globally, particularly in addressing the 6th goal of the United Nation's Sustainable Development Goals (UN SDGs). This study focuses on the blue water footprint (WFblue) assessment and prediction of WTP located at the Kuantan River Basin, Malaysia. The intake water of WTP is directly obtained from the mainstream river within the basin known as the Kuantan River. The predictability of the WFblue was evaluated by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Different hyperparameters of both the ANN and SVM models were investigated to ascertain the best prediction models attainable by evaluating both the mean squared error (MSE) as well as the coefficient of determination, R. It was demonstrated from the study that the optimised ANN model is able to yield a better prediction performance in comparison to the optimised SVM model. Therefore, it could be concluded that the application of ANN to predict the future trend is pertinent and should be incorporated in water footprint studies as it is vital for water resources regulators to anticipate the condition of WFblue in the future and to line up the appropriate actions especially in controlling the influencing parameters namely, water intake, rainfall and evaporation.

Item Type:	Article
Uncontrolled Keywords:	Blue water footprint; Artificial neural networks; Support vector machine; Hyperparameter optimization; Water resource management; Water footprint
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery
Faculty/Division:	College of Engineering Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Dr. Edriyana A. Aziz
Date Deposited:	13 Apr 2022 07:27
Last Modified:	13 Apr 2022 07:27
URI:	http://umpir.ump.edu.my/id/eprint/32928
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