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Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)
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 (R 2). It was found that the best kernel function for the SVM model was polynomial kernel function with R 2 of 0.8796. Furthermore, the LS-SVM model that trained with correct predictors had higher accuracy with R 2 of 0.9227 as compared with SVM model that trained with all the predictors with R 2 of 0.9184. The SSE and MSSE are 74.78 and 1.5594, 1.6454 for LS-SVM and SVM respectively.
Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)
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 (R 2). It was found that the best kernel function for the SVM model was polynomial kernel function with R 2 of 0.8796. Furthermore, the LS-SVM model that trained with correct predictors had higher accuracy with R 2 of 0.9227 as compared with SVM model that trained with all the predictors with R 2 of 0.9184. The SSE and MSSE are 74.78 and 1.5594, 1.6454 for LS-SVM and SVM respectively.
Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)
Leong, Wei Cong (author) / Bahadori, Alireza (author) / Zhang, Jie (author) / Ahmad, Z. (author)
International Journal of River Basin Management ; 19 ; 149-156
2021-04-03
8 pages
Article (Journal)
Electronic Resource
Unknown
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