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Prediction of Water Quality Index (WQI) using Support Vector Machine (SVM) and Least Square-Support Vector Machine (LS-SVM)

Lookup NU author(s): Dr Jie ZhangORCiD, Zainal Ahmad



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Taylor & Francis, 2019.

For re-use rights please refer to the publisher's terms and conditions.


The current calculations of water quality index (WQI) were sometimes can be very complex and time-consuming which involves sub-index calculation like BOD and COD, however with the support vector machine (SVM) and least squares support vector machine (LS-SVM) models, the WQI can be predicted immediately using directly measured physical data by using the same predictors used in the numerical approach without any sub-index calculation. There were three main parameters that control the performance of the SVM model however only the type of kernel function was investigated, they were linear, radial basis function (RBF) and polynomial kernel functions. The results of the model were then analysed by using sum squares error (SSE), mean of sum squares error (MSSE) and coefficient of determination (R2). It was found that the best kernel function for the SVM model was polynomial kernel function with R2 of 0.8796. Furthermore, LS-SVM model that trained with correct predictors had higher accuracy with R2 of 0.9227 as compared with SVM model that trained with all the predictors with R2 of 0.9184. The SSE and MSSE are 74.78 and 1.5594, 1.6454 for LS-SVM and SVM respectively.

Publication metadata

Author(s): Cong LW, Bahadori A, Zhang J, Ahmad Z

Publication type: Article

Publication status: Published

Journal: International Journal of River Basin Management

Year: 2019

Volume: 13

Issue: 6

Pages: 1310-1318

Print publication date: 01/12/2019

Online publication date: 06/06/2019

Acceptance date: 04/06/2019

Date deposited: 10/06/2019

ISSN (print): 1571-5124

ISSN (electronic): 1814-2060

Publisher: Taylor & Francis


DOI: 10.1080/15715124.2019.1628030


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