Abstract
This article deals with searching for dependences within the System of Measuring Stations.Weather measuring stations represent one of the most important data sources. The same could be said about stations that measure the composition of air and the level of pollutants. Knowledge of the current state of air quality resulting from the measured values is essential for citizens, especially in areas affected by heavy industry or dense traffic. Computation of such air quality indicators depends on values obtained from measuring stations which are more or less reliable. They can have failures or they can measure just a part of the required values. In general, searching for dependences represents a complex and non-linear problem that can be effectively solved by some class of evolutionary algorithms. This article describes a method that helps us to predict the levels of air quality in the case of station failure or data loss. The model is constructed by the symbolic regression with usage of the principles of genetic algorithms. The level of air quality of a given station is predicted with respect to a set of surrounding stations. All experiments were focused on real data obtained from the system of stations located in the Czech republic.
This work was supported by the Bio-Inspired Methods: research, development and knowledge transfer project, reg. no. CZ.1.07/2.3.00/20.0073 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic and IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by Operational Programme ’Research and Development for Innovations’ funded by Structural Funds of the European Union and state budget of the Czech Republic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fang, H., Ross, P., Corne, D.: Genetic algorithms for timetabling and scheduling (1994), http://www.asap.cs.nott.ac.uk/ASAP/ttg/resources.html
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)
Huang, C., Li, G., Xu, Z., Yu, A., Chang, L.: Design of optimal digital lattice filter structures based on genetic algorithm. Signal Processing 92(4), 989–998 (2012)
hydrometeorological institute, C.: Web page with data specification and access (2012), http://www.chmi.cz/
Ishibuchi, H., Nakashima, Y., Nojima, Y.: Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft Comput. 15(12), 2415–2434 (2011)
Juzoji, H., Nakajima, I., Kitano, T.: A development of network topology of wireless packet communications for disaster situation with genetic algorithms or with dijkstra’s. In: ICC, pp. 1–5 (2011)
Lave, L.B., Seskin, E.P.: Air pollution and human health. Science 169(3947), 723–733 (1970) doi:10.1126/science.169.3947.723, http://dx.doi.org/10.1126/science.169.3947.723 ,
Melanie, M.: An Introduction to Genetic Algorithms. A Bradford Book, MIT-Press (1999)
Melanie, M., Stephanie, F.: Genetic algorithms and artificial life. Santa Fe Institute, working Paper 93-11-072 (1994)
Pan, S.T.: A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter. Digital Signal Processing 20(2), 314–327 (2010)
Park, B.J., Choi, H.R.: A genetic algorithm for integration of process planning and scheduling in a job shop. In: Australian Conference on Artificial Intelligence, pp. 647–657 (2006)
Sedighi, K.H., Manikas, T.W., Ashenayi, K., Wainwright, R.L.: A genetic algorithm for autonomous navigation using variable-monotone paths. I. J. Robotics and Automation 24(4) (2009)
Seinfeld, J.H., Pandis, S.N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd edn. Wiley-Interscience (2006), http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0471720186
Tsang, E.P.K., Warwick, T.: Applying genetic algorithms to constraints satisfaction optimization problems. In: Proc. of 9th European Conf. on AI, Aiello L.C (1990)
Vardoulakis, S., Fisher, B.E., Pericleous, K., Gonzalez-Flesca, N.: Modelling air quality in street canyons: a review. Atmospheric Environment 37(2), 155–182 (2003) doi:10.1016/S1352-2310(02)00857-9, http://www.sciencedirect.com/science/article/pii/S1352231002008579 ,
Wainwright, R.L.: Introduction to genetic algorithms theory and applications. In: The Seventh Oklahoma Symposium on Artificial Intelligence (1993)
Zannetti, P.: Air pollution modeling: theories, computational methods, and available software. Computational Mechanics Publications (1990), http://books.google.cz/books?id=FyxSAAAAMAAJ
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Gajdoš, P., Radecký, M., Vozňák, M. (2013). Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-00542-3_51
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
Online ISBN: 978-3-319-00542-3
eBook Packages: EngineeringEngineering (R0)