Skip to main content
SpringerLink
Log in

Modelling PM10 Crisis Peaks Using Multi-agent Based Simulation: Application to Annaba City, North-East Algeria

  • Conference paper
  • First Online:
Information Systems for Crisis Response and Management in Mediterranean Countries (ISCRAM-med 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 233))

Abstract

The paper describes a MAS (multi-agent system) simulation approach for controlling PM10 (Particulate Matter) crisis peaks. A dispersion model is used with an Artificial Neural Network (ANN) to predict the PM10 concentration level. The dispersion and ANN models are integrated into a MAS system. PM10 source controllers are modelled as software agents. The MAS is composed of agents that cooperate with each other for reducing their emissions and control the air pollution peaks. Different control strategies are simulated and compared using data from Annaba (North-East Algeria). The simulator helps to compare and assess the efficiency of policies to control peaks in PM10.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO (World Health Organisation), Ecosystems and Human Well-being: Health Synthesis. WHO Library Cataloguing-in-Publication Data (2005)

    Google Scholar 

  2. Daly, A., Zannetti, P.: Air pollution modeling–An overview. Ambient air pollution (2007)

    Google Scholar 

  3. Lushi, E., Stockie, J.M.: An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources. Atmos. Environ. 44(8), 1097–1107 (2010)

    Article  Google Scholar 

  4. Ghazi, S., Khadir, M.T.: Combination of artificial neural network models for air quality predictions for the region of Annaba, Algeria. Int. J. Environ. Stud. 69(1), 79–89 (2012)

    Article  Google Scholar 

  5. Russo, A., Soares, A.O.: Hybrid model for urban air pollution forecasting: a stochastic spatio-temporal approach. Math. Geosci. 46(1), 75–93 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  6. Ghazi, S., Khadir, T., Dugdale, J.: Multi-agent based simulation of environmental pollution issues: a review. In: Corchado, J.M., Bajo, J., Kozlak, J., Pawlewski, P., Molina, J.M., Gaudou, B., Julian, V., Unland, R., Lopes, F., Hallenborg, K., García Teodoro, P. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 13–21. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  7. Hülsmann, F., Gerike, R., Kickhöfer, B., Nagel, K., Luz, R.: Towards a multi-agent based modeling approach for air pollutants in urban regions. In: Proceedings of the Conference on Luftqualität an Straßen, pp. 144–166 (2011)

    Google Scholar 

  8. Athanasiadis, I.N., Mitkas, P.A.: A methodology for developing environmental information systems with software agents. In: Cortés, U., Poch, M. (eds.) Advanced Agent-Based Environmental Management Systems, Whitestein Series in Software Agent Technologies and Autonomic Computing, pp 119–137. Birkhäuser Basel (2009)

    Google Scholar 

  9. Borri, D., Camarda, D.: Planning for the environmental quality of urban microclimate: a multiagent-based approach. In: Luo, Y. (ed.) CDVE 2011. LNCS, vol. 6874, pp. 129–136. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Papaleonidas, A., Iliadis, L.: Hybrid and reinforcement multi agent technology for real time air pollution monitoring. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 274–284. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Ahat, M., Amor, S.B., Bui, M., Lamure, M., Courel, M.-F.: Pollution modeling and simulation with multi-agent and pretopology. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 4, pp. 225–231. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  12. Stockie, J.M.: The mathematics of atmospheric dispersion modeling. SIAM Rev. 53(2), 349–372 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  13. Power, C.: A spatial agent-based model of n-person prisoner’s dilemma cooperation in a socio-geographic community, J. Artif. Soc. Soc. Simul. 12(18) (2009). http://jasss.soc.surrey.ac.uk/12/1/8.html

Download references

Acknowledgements

This work was funded by the Algerian Ministry of Higher Education and Scientific Research, PNE 2014/2015 Program.

Author information

Authors and Affiliations

  1. Laboratoire de Gestion Electronique de Documents, Department of Computer Science, University Badji Mokhtar, PO-Box 12, 23000, Annaba, Algeria

    Sabri Ghazi & Tarek Khadir

  2. University Grenoble Alpes, LIG, Saint-Martin-d’Hères, France

    Julie Dugdale

  3. University of Agder, Kristiansand, Norway

    Julie Dugdale

Authors
  1. Sabri Ghazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie Dugdale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tarek Khadir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sabri Ghazi .

Editor information

Editors and Affiliations

  1. University of Tunis El Manar, Institut Supérieur d’Informatique, Tunis, Tunisia

    Narjès Bellamine Ben Saoud

  2. Grenoble Informatics Laboratory, Université Grenoble-Alpes, Saint-Martin-d'Hères, France

    Carole Adam

  3. University of Toulouse 1, Toulouse, France

    Chihab Hanachi

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Ghazi, S., Dugdale, J., Khadir, T. (2015). Modelling PM10 Crisis Peaks Using Multi-agent Based Simulation: Application to Annaba City, North-East Algeria. In: Bellamine Ben Saoud, N., Adam, C., Hanachi, C. (eds) Information Systems for Crisis Response and Management in Mediterranean Countries. ISCRAM-med 2015. Lecture Notes in Business Information Processing, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-24399-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24399-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24398-6

  • Online ISBN: 978-3-319-24399-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation