SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting

The application of Artificial Intelligence (AI) has been upgraded in many scientific fields the last years, with the development of new artificial intelligence-based technologies and techniques. Considering that in the literature there is a very limited number of studies proposing and testing new SV...

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Vydané v:Applied soft computing Ročník 93; s. 106410
Hlavný autor: Kouziokas, Georgios N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.08.2020
Predmet:
Artificial neural networks
Bayesian optimization
Environmental pollutant
Particle swarm optimization
Support vector machines
Support vector machines
Environmental pollutant
Bayesian optimization
Particle swarm optimization
Artificial neural networks
ISSN:1568-4946, 1872-9681
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Popis
Shrnutí:The application of Artificial Intelligence (AI) has been upgraded in many scientific fields the last years, with the development of new artificial intelligence-based technologies and techniques. Considering that in the literature there is a very limited number of studies proposing and testing new SVM kernels in regression problems, this research introduces a novel SVM Kernel by incorporating a transformed particle swarm optimized ANN weight vector in a Bayesian optimized SVM kernel in a time series problem for predicting the atmospheric pollutant factor Particulate Matter 10 (PM10). The proposed model introduces a new SVM kernel that illustrates an increased forecasting accuracy compared to the conventional optimized ANN and SVM models according to the experimental results. The findings of the proposed methodology illustrate that the new proposed SVM Kernel can be utilized as an improved forecasting technique. •A new SVM kernel is proposed based on PSO Optimized Vector and Bayesian Optimized SVM.•The transformed weight vector of the PSO-ANN enhanced the predictability in the new SVM kernel.•The proposed SVM kernel produced significantly improved forecasting results compared to the conventional methods.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2020.106410