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

Abstract

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.