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Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks

Environmental Science and Pollution Research volume 23, pages 1634–1641 (2016)Cite this article

Abstract

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9 % has been reached.

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Acknowledgments

The authors would like to thank the Algerian National Observatory of Environment and Sustainable Development (ONEDD) for providing access to the meteorological data used in this study.

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Affiliations

  1. Laboratory of Environmental science and Material studies, University of Oran 1-Ahmed Benbella, Oran, Algeria

    Hamza Abderrahim, Mohammed Reda Chellali & Ahmed Hamou

  2. Hydrometeorological Institute for Training and Research-IHFR, Oran, Algeria

    Hamza Abderrahim

  3. Faculty of Materials Science and Technology, Slovak University of Technology in Bratislava, Paulinska 16, 917 24, Trnava, Slovakia

    Mohammed Reda Chellali

  4. Institute of Materials Physics, University of Muenster, Wilhelm-Klemm-Str. 10, 48149, Muenster, Germany

    Mohammed Reda Chellali

Authors
  1. Hamza Abderrahim
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  2. Mohammed Reda Chellali
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  3. Ahmed Hamou
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Corresponding author

Correspondence to Mohammed Reda Chellali.

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Responsible editor: Gerhard Lammel

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Abderrahim, H., Chellali, M.R. & Hamou, A. Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks. Environ Sci Pollut Res 23, 1634–1641 (2016). https://doi.org/10.1007/s11356-015-5406-6

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  • DOI: https://doi.org/10.1007/s11356-015-5406-6

Keywords

  • Pollution
  • Neural network
  • Multilayer perceptron
  • PM10