Collective neural networks system for PM<inf>10</inf> classification in the north of Thailand

Abstract

© 2018 IEEE. Air contamination is one of the primary issues in the world. PM10 is the major pollutant having highly affecting in human wellbeing. Numerous scientists around the world create many classifications and prediction model to conjecture PM10 for alarm people in their country. Consistently from February to May in the northern part of Thailand, there is the exhaust cloud issue of PM10 yet there are few pieces of research in the air pollution utilizing the up to date data set. By observing this issue, refreshed information between 2011 and 2017 are utilized. Only two of the data from stations in Lampang and Phayao were selected for this study. Due to the minimal data loss problem. The collective neural networks system is selected to create an appropriate classification model. The average accuracy of prediction results in this work is 92.51% which higher than related works in a similar topic.