Improved support vector machine using optimization techniques for an aerobic granular sludge

Aerobic granular sludge (AGS) is one of the treatment methods often used in
wastewater systems. The dynamic behavior of AGS is complex and hard to
predict especially when it comes to a limited data set. Theoretically, support
vector machine (SVM) is a good prediction tool in handling limited data set.
In this paper, an improved SVM using optimization approaches for better
predictions is proposed. Two different types of optimization are built which
are particle swarm optimization (PSO) and genetic algorithm (GA).
The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid
Search are developed and compared prior to several feature analysis for
verification purposes. The experimental data under hot temperature of 50˚C
obtained from sequencing batch reactor is used. From simulation results,
the proposed SVM with optimizations improve the prediction of chemical oxygen
demand compared to the conventional grid search method and hence provide
better prediction of effluent quality using AGS wastewater treatment systems.