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.
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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.
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.
Breiman, L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231.
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.
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.
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.
Castelli, M., Silva, S., & Vanneschi, L. (2015b). A C++ framework for geometric semantic genetic programming. Genetic Programming and Evolvable Machines, 16(1), 73–81.
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.
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.
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.
Chan, C.K., & Yao, X. (2008). Air pollution in mega cities in china. Atmospheric environment, 42(1), 1–42.
Corbette, J. (2013). Using information systems to improve energy efficiency: Do smart meters make a difference Information Systems Frontiers, 15(5), 747–760.
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods. New York: Cambridge University Press.
Gonçalves, I., Silva, S., & Fonseca, C.M. (2015). On the generalization ability of geometric semantic genetic programming. In Genetic Programming, Springer, pp 41–52.
Haykin, S. (1999). Neural networks: a comprehensive foundation: Prentice Hall.
Hoffmann, L. (2009). Multivariate Isotonic Regression and Its Algorithms. Wichita State University, College of Liberal Arts and Sciences, Department of Mathematics and Statistics.
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.
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.
Kampa, M., & Castanas, E. (2008). Human health effects of air pollution. Environmental pollution, 151(2), 362–367.
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.
Kim, K.H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment international, 74, 136–143.
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.
Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. USA: MIT Press, Cambridge.
Koza, J.R. (2010). Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11(3-4), 251–284.
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.
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.
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.
Li, D., Xu, L., & Zhao, S. (2015). The internet of things: a survey. Information Systems Frontiers, 17(2), 243–259.
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.
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.
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.
Qin, H., & Liao, T.F. (2015). The association between rural–urban migration flows and urban air quality in china. Regional Environmental Change, 1–13.
Seber, G., & Wild, C. (2003). Nonlinear Regression. Wiley Series in Probability and Statistics. Wiley.
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.
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.
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.
United Nations, Department of Economic and Social Affairs, Population Division (2014). World urbanization prospects: The 2014 revision, highlights.
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.
Vanneschi, L., Castelli, M., & Silva, S. (2014). A survey of semantic methods in genetic programming. Genetic Programming and Evolvable Machines, 15(2), 195–214.
Weka Machine Learning Project (2015). Weka. http://www.cs.waikato.ac.nz/ml/weka.
World Health Organization (2014). Review of evidence on health aspects of air pollution.
World Health Organization (2015). Reducing global health risks through mitigation of short-lived climate pollutants.
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.
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Castelli, M., Gonçalves, I., Trujillo, L. et al. An evolutionary system for ozone concentration forecasting. Inf Syst Front 19, 1123–1132 (2017). https://doi.org/10.1007/s10796-016-9706-2
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DOI: https://doi.org/10.1007/s10796-016-9706-2