Abstract
Air quality is one of the main priorities for the improvement of the life quality in urban regions, as air pollution is usually, concentrated in such densely populated areas. Most of the countries have a national air quality monitoring network that allow an analysis of the air quality status, especially for urban regions that are nodes in this network. As the network is geographically distributed, it can be mapped in a natural way on an intelligent agents based system. The paper describes the modeling framework of an air quality monitoring and analysis multiagent system for urban regions.
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References
Athanasiadis, I.N., Mitkas, P.A.: Knowledge Discovery for Operational Decision Support in Air Quality Management. Journal of Environmental Informatics 9(2), 100–107 (2007)
Athanasiadis, I.N., Mitkas, P.A.: An agent-based intelligent environmental monitoring system. Management of Environmental Quality 15(3), 238–249 (2004)
Di Lecce, V., Pasquale, C., Piuri, V.: A basic ontology for multi agent system communication in an environmental monitoring system. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Boston, pp. 45–50 (2004)
Kalapanidas, E., Avouris, N.: Air Quality Management Using a Multi-Agent System. Computer-Aided Civil and Infrastructure Engineering 17, 119–130 (2002)
Karatzas, K.D.: Artificial Intelligence Applications in the Atmospheric Environment: Status and Future Trends. Environmental Engineering Management Journal 9(2), 171–180 (2010)
Kim, Y.J., Platt, U. (eds.): Advanced Environmental Monitoring. Springer (2008)
Kolehmainen, M., Martikainen, H., Ruuskanen, J.: Neural networks and periodic components used in air quality forecasting. Atmospheric Environment 35, 815–825 (2001)
Moussiopoulos, N. (ed.): Air Quality in Cities. Springer, Berlin (2003)
Noori, R., Hoshyaripour, G., Ashrafi, K., Araabi, B.N.: Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment 44(4), 476–482 (2010)
Núñez, H., Sànchez-Marrè, M., Cortés, U., Comas, J., Martinez, M., Rodríquez-Roda, I., Poch, M.: A comparative study on the use of similarity measures in case-based reasoning to improve the classsification of environmental system situations. Environmental Modelling & Software 9(9), 809–819 (2004)
Oprea, M.: INTELLEnvQ-Air: An Intelligent System for Air Quality Analysis in Urban Regions. International Journal of Artificial Intelligence 9(A12) (2012)
Oprea, M.: A case study of knowledge modelling in an air pollution control decision support system. AiCommunications 18(4), 293–303 (2005)
Oprea, M., Nichita, C.: On the Distributed Water Pollution Control Solving with an Agent-Based Approach. In: Badica, C., Paprzycki, M. (eds.) Advances in Intelligent and Distributed Computing. SCI, vol. 78, pp. 289–294. Springer, Heidelberg (2008)
Polat, K.: A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS). In: Neural Computing & Applications. Springer (2011), doi:10.1007/s00521-011-0602-x
Sànchez-Marrè, M., Gibert, K., Sevilla, B.: Evolving GESCONDA to an Intelligent Decision Support Tool. In: Proceedings of the International Congress on Environmental Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada (2010)
Weiss, G.: Multiagent Systems: A Modern Introduction to Distributed Artificial Intelligence. MIT Press, Cambridge (1999)
Zeus Toolkit, http://www.labs.bt.com/projects/agents/zeus/
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Oprea, M. (2012). Agent-Based Modeling of an Air Quality Monitoring and Analysis System for Urban Regions. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds) Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33412-2_38
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DOI: https://doi.org/10.1007/978-3-642-33412-2_38
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