Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)
Date
2013
Authors
Ahmad Zia Ul-Saufie
Ahmad Shukri Yahaya
Nor Azam Ramli
Norrimi Rosaida
Hazrul Abdul Hamid
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Future 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.
Description
Keywords
Principle component analysis, Feedforward backpropagation, Multiple linear regression, Future prediction, PM10 concentrations
Citation
Ul-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.