Information Systems Frontiers

, Volume 19, Issue 5, pp 1123–1132 | Cite as

An evolutionary system for ozone concentration forecasting

  • Mauro Castelli
  • Ivo Gonçalves
  • Leonardo Trujillo
  • Aleš Popovič
Article

Abstract

Nowadays, with more than 50 % of the world’s population living in urban areas, cities are facing important environmental challenges. Among them, air pollution has emerged as one of the most important concerns, taking into account the social costs related to the effect of polluted air. According to a report of the World Health Organization, approximately seven million people die each year from the effects of air pollution. Despite this fact, the same report suggests that cities could greatly improve their air quality through local measures by exploiting modern and efficient solutions for smart infrastructures. Ideally, this approach requires insights of how pollutant levels change over time in specific locations. To tackle this problem, we present an evolutionary system for the prediction of pollutants levels based on a recently proposed variant of genetic programming. This system is designed to predict the amount of ozone level, based on the concentration of other pollutants collected by sensors disposed in critical areas of a city. An analysis of data related to the region of Yuen Long (one of the most polluted areas of China), shows the suitability of the proposed system for addressing the problem at hand. In particular, the system is able to predict the ozone level with greater accuracy with respect to other techniques that are commonly used to tackle similar forecasting problems.

Keywords

Evolutionary computation Genetic programming Smart cities Forecasting Air quality 

References

  1. Anderson, J.O., Thundiyil, J.G., & Stolbach, A. (2012). Clearing the air: a review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8(2), 166– 175.CrossRefGoogle Scholar
  2. Ayres, J.G. (2010). The mortality effects of long-term exposure to particulate air pollution in the united kingdom. Report by the Committee on the Medical Effects of Air Pollutants.Google Scholar
  3. Breiman, L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231.CrossRefGoogle Scholar
  4. Castelli, M., Castaldi, D., Giordani, I., Silva, S., Vanneschi, L., Archetti, F., & Maccagnola, D. (2013). An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. In Progress in Artificial Intelligence, Springer, pp 78–89.Google Scholar
  5. Castelli, M., Vanneschi, L., & Silva, S. (2014). Prediction of the unified Parkinson’s disease rating scale assessment using a genetic programming system with geometric semantic genetic operators. Expert Systems with Applications, 41(10), 4608–4616.CrossRefGoogle Scholar
  6. Castelli, M., Manzoni, L., Vanneschi, L., Silva, S., & Popovič, A (2016a). Self-tuning geometric semantic genetic programming. Genetic Programming and Evolvable Machines, 17(1), 55– 74.Google Scholar
  7. Castelli, M., Silva, S., & Vanneschi, L. (2015b). A C++ framework for geometric semantic genetic programming. Genetic Programming and Evolvable Machines, 16(1), 73–81.Google Scholar
  8. Castelli, M., Trujillo, L., Vanneschi, L., & Popoviċ, A (2015c). Prediction of energy performance of residential buildings: a genetic programming approach. Energy and Buildings, 102, 67–74.Google Scholar
  9. Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., & Legrand, P. (2015d). Geometric semantic genetic programming with local search. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, USA, GECCO ’15, pp 999–1006.Google Scholar
  10. Castelli, M., Vanneschi, L., & De Felice, M. (2015e). Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The south Italy case. Energy Economics, 47, 37–41.Google Scholar
  11. Chan, C.K., & Yao, X. (2008). Air pollution in mega cities in china. Atmospheric environment, 42(1), 1–42.CrossRefGoogle Scholar
  12. Corbette, J. (2013). Using information systems to improve energy efficiency: Do smart meters make a difference Information Systems Frontiers, 15(5), 747–760.CrossRefGoogle Scholar
  13. Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods. New York: Cambridge University Press.CrossRefGoogle Scholar
  14. Gonçalves, I., Silva, S., & Fonseca, C.M. (2015). On the generalization ability of geometric semantic genetic programming. In Genetic Programming, Springer, pp 41–52.Google Scholar
  15. Haykin, S. (1999). Neural networks: a comprehensive foundation: Prentice Hall.Google Scholar
  16. Hoffmann, L. (2009). Multivariate Isotonic Regression and Its Algorithms. Wichita State University, College of Liberal Arts and Sciences, Department of Mathematics and Statistics.Google Scholar
  17. Hota, C., Upadhyaya, S., & Al-Karaki, J. (2015). Advances in secure knowledge management in the big data era. Information Systems Frontiers, 17(5), 983–986.CrossRefGoogle Scholar
  18. Ji, D., Li, L., Wang, Y., Zhang, J., Cheng, M., Sun, Y., Liu, Z., Wang, L., Tang, G., Hu, B., & et al. (2014). The heaviest particulate air-pollution episodes occurred in northern china in january, 2013: insights gained from observation. Atmospheric Environment, 92, 546–556.CrossRefGoogle Scholar
  19. Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental pollution, 151(2), 362–367.CrossRefGoogle Scholar
  20. Karatzas, K.D., & Kaltsatos, S. (2007). Air pollution modelling with the aid of computational intelligence methods in thessaloniki, greece. Simulation Modelling Practice and Theory, 15(10), 1310–1319.CrossRefGoogle Scholar
  21. Kim, K.H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment international, 74, 136–143.CrossRefGoogle Scholar
  22. Kittelson, D., Watts, W., & Johnson, J. (2004). Nanoparticle emissions on minnesota highways. Atmospheric Environment, 38(1), 9–19. doi:10.1016/j.atmosenv.2003.09.037.CrossRefGoogle Scholar
  23. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. USA: MIT Press, Cambridge.Google Scholar
  24. Koza, J.R. (2010). Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11(3-4), 251–284.CrossRefGoogle Scholar
  25. Krawiec, K., & Lichocki, P. (2009). Approximating geometric crossover in semantic space. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM, pp 987–994.Google Scholar
  26. Kumar, P., & Thiele, L. (2014). p-yds algorithm: An optimal extension of yds algorithm to minimize expected energy for real-time jobs. In Proceedings of the 14th International Conference on Embedded Software, ACM, New York, NY, USA, EMSOFT ’14, pp 12:1–12:10. doi:10.1145/2656045.2656065.
  27. Kumar, P., Jain, S., Gurjar, B., Sharma, P., Khare, M., Morawska, L., & Britter, R. (2013). New directions: Can a ”blue sky“ return to indian megacities Atmospheric Environment, 71, 198–201. doi:10.1016/j.atmosenv.2013.01.055.CrossRefGoogle Scholar
  28. Li, D., Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers, 17(2), 243–259.CrossRefGoogle Scholar
  29. Lim, S., & et al. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010. The Lancet, 380, 2224–2260.CrossRefGoogle Scholar
  30. Medina, S., Plasencia, A., Ballester, F., Mücke, H G, & Schwartz, J. (2004). Apheis: public health impact of pm10 in 19 european cities. Journal of Epidemiology and Community Health, 58(10), 831–836. doi:10.1136/jech.2003.016386.CrossRefGoogle Scholar
  31. Moraglio, A., Krawiec, K., & Johnson, C.G. (2012). Geometric semantic genetic programming. In Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., & Pavone, M. (Eds.) Parallel Problem Solving from Nature, PPSN XII (part 1), Springer, Lecture Notes in Computer Science, vol 7491, pp 21–31.Google Scholar
  32. Qin, H., & Liao, T.F. (2015). The association between rural–urban migration flows and urban air quality in china. Regional Environmental Change, 1–13.Google Scholar
  33. Seber, G., & Wild, C. (2003). Nonlinear Regression. Wiley Series in Probability and Statistics. Wiley.Google Scholar
  34. Sharma, P., Sharma, P., Jain, S., & Kumar, P. (2013). An integrated statistical approach for evaluating the exceedence of criteria pollutants in the ambient air of megacity delhi. Atmospheric Environment, 70(0), 7–17.Google Scholar
  35. Sousa, S., Martins, F., Alvim-Ferraz, M., & Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software, 22(1), 97–103.CrossRefGoogle Scholar
  36. Stadler, P. (1995). Towards a theory of landscapes. In: Complex Systems and Binary Networks. Lecture Notes in Physics, 461-461, 78–163. Springer Berlin Heidelberg.CrossRefGoogle Scholar
  37. United Nations, Department of Economic and Social Affairs, Population Division (2014). World urbanization prospects: The 2014 revision, highlights.Google Scholar
  38. Vanneschi, L., Silva, S., Castelli, M., & Manzoni, L. (2013). Geometric Semantic Genetic Programming for Real Life Applications. In Genetic Programming Theory and Practice XI GPTP 2013, University of Michigan, Ann Arbor, May 9-11, 2013, pp 191–209.Google Scholar
  39. Vanneschi, L., Castelli, M., & Silva, S. (2014). A survey of semantic methods in genetic programming. Genetic Programming and Evolvable Machines, 15(2), 195–214.CrossRefGoogle Scholar
  40. Weka Machine Learning Project (2015). Weka. http://www.cs.waikato.ac.nz/ml/weka.
  41. World Health Organization (2014). Review of evidence on health aspects of air pollution.Google Scholar
  42. World Health Organization (2015). Reducing global health risks through mitigation of short-lived climate pollutants.Google Scholar
  43. Zhang, Q., & Crooks, R. (2012). Toward an environmentally sustainable future: Country environmental analysis of the people’s republic of China: Report of the Asian Development Bank.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mauro Castelli
    • 1
  • Ivo Gonçalves
    • 1
    • 2
  • Leonardo Trujillo
    • 3
  • Aleš Popovič
    • 1
    • 4
  1. 1.NOVA IMSUniversidade Nova de LisboaLisbonPortugal
  2. 2.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.Tree-Laboratory, Instituto Tecnológico de TijuanaTijuanaMéxico
  4. 4.University of Ljubljana, Faculty of EconomicsLjubljanaSlovenia

Personalised recommendations