Abstract
The prediction of the ground ozone is necessary daily, to inform the population and to allow measures to be taken for the reduction of the ozone concentration at a one-hour exceeding of the ozone value of 180µg/m 3.
After the presentation and comparison of applied methods of the ozone prognosis we develop a fuzzy approach, by which the process of rule formation is supported by a special neural network.
With ground measurements of ozone, nitrogen oxides and the meteorological series of temperature, humidity, clouding over and wind from 3 Saxon measuring places there are generated and tested optimal knowledge bases by the developed Fuzzy-Multilayer Perceptron.
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
BECHER, T. and SCHMIDT, T. (1994): Prediction of Summer and Winter Smog with Neural Nets. In: L.M. Hilty (ed.): Informatics for the environmental protection. Bd. I, Metropolis, Marburg, 387–394.
FLEMMING, J. (1996): Study on the Initialisation of the Photo-chemical Spreading Model with Measuring Ozone Concentration. Diploma Paper, FU Berlin, FB Geological Science, Institute for Meteorology.
HARTMANN, C. (1993): Smog Prediction with Neural Nets. Diploma Paper, TU Berlin.
LIN, C.T. and LEE, C.S.G. (1994): Supervised and Unsupervised Learning with Fuzzy Similarity for Neural Network-Based Fuzzy Logic Control Systems. In: R.R. Yager and L.A. Zadeh (eds): Fuzzy Sets, Neural Networks and Soft Computing. Van Nostrand Reinhold, New York, 85–125.
LIN, C.T. and LEE, C.S.G. (1996): Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall, London/New York.
REIMER, E., WEISS, W. and BERENDORF, K. (1996): Ozone Prognosis by the Use of TRIP and Fuzzy-Logic. FU Berlin, Institute for Meteorology.
SCHULZE, R. (1997): Fuzzy-Rule Generation in Neural Architecture by the Example of the Ozone Prognosis. Diploma Paper, TU Dresden, Institute for Artificial Intelligence.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Gärtner, K., Schulze, R. (1999). Data Mining with a Neuro-Fuzzy Model for the Ozone Prognosis. In: Gaul, W., Locarek-Junge, H. (eds) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60187-3_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-60187-3_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65855-9
Online ISBN: 978-3-642-60187-3
eBook Packages: Springer Book Archive