Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study,...

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Bibliographic Details
Published in:Water science & technology. Water supply Vol. 22; no. 2; pp. 1951 - 1963
Main Authors: Chia, See Leng, Chia, Min Yan, Koo, Chai Hoon, Huang, Yuk Feng
Format: Journal Article
Language:English
Published: London IWA Publishing 01.02.2022
Subjects:
advanced optimization algorithm
Algorithms
Chemical oxygen demand
Genetic algorithms
hybrid optimized lssvm model
kernel function
Kernel functions
Lagrange multiplier
Least squares
Machine learning
Mutation
Optimization algorithms
Particle swarm optimization
Predictions
river water quality parameter
Rivers
Root-mean-square errors
Support vector machines
Water quality
water quality index
Selangor Malaysia
Malaysia
ISSN:1606-9749, 1607-0798
Online Access:Get full text
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Summary:Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of the water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.
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ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2021.303