Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)

Ahmad Zia Ul-SaufieAhmad Shukri YahayaNor Azam RamliNorrimi RosaidaHazrul Abdul Hamid2024-05-102024-05-102013Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., & Hamid, H. A. (2013). Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA). Atmospheric Environment, 77, 621-630.https://repoemc.ukm.my/handle/123456789/579Future PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia.enPrinciple component analysisFeedforward backpropagationMultiple linear regressionFuture predictionPM10 concentrationsFuture daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)Journal