文章基本信息

标题：PM₁₀ Concentrations Short Term Prediction Using Feedforward Backpropagation and General Regression Neural Network in a Sub-urban Area
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作者：Ahmad Zia Ul- Saufie ; Ahmad Shukri Yahaya ; Nor Azam Ramli 等
期刊名称：Journal of Environmental Science and Technology
印刷版ISSN：1994-7887
电子版ISSN：2077-2181
出版年度：2015
卷号：8
期号：2
页码：59-73
DOI：10.3923/jest.2015.59.73
出版社：Asian Network for Scientific Information
摘要：Particulate matter has a significant impact on human health when the concentration levels of this substance exceed Malaysia Ambient Air Quality Guidelines (MAAQG). This research focused only on particulate matter with an aerodynamic diameter less than 10 μm, namely PM₁₀. Statistical modeling is required to predict future PM₁₀ concentration. The aim of this study is to develop and predict next day, next two-day and next three-day PM₁₀ concentration in a sub-urban area (Seberang Jaya) of Malaysia. This study used daily average monitoring records from 2001 to 2010. Two main models for predicting PM₁₀concentration were used: feedforward backpropagation and general regression neural network models. The models for the artificial neural network show that feedforward backpropagation is better than the general regression neural network with fewer errors; as much as 5.6% for next day, 3.5% for next two-day and 2.5% for next three-day predictions. These models will help local authorities to take an appropriate course of action to reduce PM₁₀ concentration and could also be used as an early warning system.