Open AccessImproved support vector machine using optimization techniques for an aerobic granular sludge
Author(s) -
Nur Sakinah Ahmad Yasmin,
Norhaliza Abdul Wahab,
Aznah Nor Anuar
Publication year - 2020
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v9i5.2264
Subject(s) - support vector machine , particle swarm optimization , hyperparameter optimization , genetic algorithm , computer science , set (abstract data type) , data mining , artificial intelligence , pattern recognition (psychology) , machine learning , programming language
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.