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Abstract
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Article Information:
PM10 Forecasting Using Soft Computing Techniques
Mohammad F. Ababneh, Ala’a O. AL-Manaseer and Mohammad Hjouj Btoush
Corresponding Author: Mohammad F. Ababneh
Submitted: July 22, 2013
Accepted: August 03, 2013
Published: April 25, 2014 |
Abstract:
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Air quality forecasting has acquired great significance in environmental sciences due to its ad¬verse affects on humans and the environment. The artificial neural network is one of the most common soft computing techniques that can be applied for modeling such complex problem. This study designed air quality forecasting model using three-layer FFNN's and recurrent Elman network to forecast PM10 air pol¬lutant concentrations 1 day advance in Yilan County, Taiwan. Then, the optimal model is selected based on testing performance measurements (RMSE, MAE, r, IA and VAF) and learning time. This study used an hourly historical data set from 1/1/2009-31/12/2011 collected by Dongshan station. The data was entirely pre-processed and cleared form missing and outlier values then transformed into daily average values. The final results showed that the three-layer FFNN with One Step Secant (OSS) training algorithm achieved better results than Elman network with Gradient Descent adaptive learning rate (GDX) training al¬gorithm. Where, the FFNN required the less training time and achieved better perfor-mance in forecasting PM10 concentrations. Also, the testing performance measurements shown that the selected daily average input variables in previous day (PM2.5), relative humidity, PM10, temperature, wind direction and speed is critical to give better forecasting accuracy. Whereas, the testing measurements RMSE = 6.23 µg/m3, MAE = 4.75 &mug/m3, r = 0.943, IA = 0.964 and VAF = 88.80 in PM10 FFNN forecasting model that used OSS training algorithm.
Key words: Forecasting, neural networks, soft computing, , , ,
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Cite this Reference:
Mohammad F. Ababneh, Ala’a O. AL-Manaseer and Mohammad Hjouj Btoush, . PM10 Forecasting Using Soft Computing Techniques. Research Journal of Applied Sciences, Engineering and Technology, (16): 3253-3265.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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