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

Abstract

Abstract 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.