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Journal of Environmental Science and Technology
  Year: 2015 | Volume: 8 | Issue: 2 | Page No.: 59-73
DOI: 10.3923/jest.2015.59.73
 
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PM10 Concentrations Short Term Prediction Using Feedforward Backpropagation and General Regression Neural Network in a Sub-urban Area

Ahmad Zia Ul- Saufie, Ahmad Shukri Yahaya, Nor Azam Ramli and Hazrul Abdul Hamid

Abstract:
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 PM10 concentration 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.
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How to cite this article:

Ahmad Zia Ul- Saufie, Ahmad Shukri Yahaya, Nor Azam Ramli and Hazrul Abdul Hamid, 2015. PM10 Concentrations Short Term Prediction Using Feedforward Backpropagation and General Regression Neural Network in a Sub-urban Area. Journal of Environmental Science and Technology, 8: 59-73.

DOI: 10.3923/jest.2015.59.73

URL: https://scialert.net/abstract/?doi=jest.2015.59.73

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