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Short-run prediction of suspended particles’ pollution; a neural network application: Case study: Ahvaz city, Iran

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Research Paper 01/03/2014
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Short-run prediction of suspended particles’ pollution; a neural network application: Case study: Ahvaz city, Iran

Dr Hossein Sadeghi, Samaneh Khaksar Astaneh, Mohammadhadi Hajian
J. Bio. Env. Sci.4( 3), 25-31, March 2014.
Certificate: JBES 2014 [Generate Certificate]
Key: ForecastLag Neural NetworkLMS learning algorithmPM10Pollution alleviation

Abstract

In order to take proper policies for pollution alleviation, it is necessary to forecast the trend of air pollution. Based on reports of World Health Organization, Ahvaz, a city located in south of Iran, is the most polluted city in the world. In this study, we employ daily time series data of maximum suspended PM10, a 10-micron suspended particle causing intensive air pollution, in order to predict pollution volume of PM10 in the air of Ahvaz city. For which, we used LMS learning algorithm to design a lag network by which concentration of PM10 is predicted for October of 2011. The results indicate that the average amount of the pollutant in this month is 482 microgram per square meter, and the maximum and minimum concentrations are respectively 722 and 319 microgram per square meter which are many times more than the maximum amount, 20 microgram per square meter, which is recognized by WHO. To control this phenomenon undoubtedly demands attention and cooperation of neighboring countries.

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