title: Improving Neural Network Prediction Accuracy for PM10Individual Air Quality Index Pollution Levels.
abstract: Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters <10 μm (PM10) as adjudicated by the Individual Air Quality Index (IAQI) on fugitive dust from nearby construction sites. To combat this problem, the Construction Influence Index (Ci) is introduced in this article to improve forecasting models based on three neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM10IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination andAHPC(the accuracy rate of high PM10IAQI caused by nearby construction activity) compared to the original models when predicting high PM10IAQI levels attributable to fugitive dust from nearby construction sites.
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Qi
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China .
authors.forename: Shengjun
authors.initials: S
authors.lastname: Wu
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Yun
authors.initials: Y
authors.lastname: Du
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Huaiping
authors.initials: H
authors.lastname: Xue
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Fei
authors.initials: F
authors.lastname: Xiao
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Xuan
authors.initials: X
authors.lastname: Ban
authors.affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China .
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Li
keywords: PM10; construction site; fugitive dust; neural network; pollution