Title: | Data mining methods for prediction of air pollution |
Authors: | Siwek, Krzysztof Osowski, Stanislaw |
Citation: | ISTET 2013: International Symposiumon Theoretical Electrical Engineering: 24th – 26th June 2013: Pilsen, Czech Republic, p. III-13-III-14. |
Issue Date: | 2013 |
Publisher: | University of West Bohemia |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://hdl.handle.net/11025/11487 |
ISBN: | 978-80-261-0246-5 |
Keywords: | data mining;znečištění vzduchu;prognózování časových řad;výběr znaků;neuronové sítě;výpočetní inteligence |
Keywords in different language: | data mining;air pollution;time series forecasting;feature selection;neural networks;computational intelligence |
Abstract: | The paper discusses the methods of data mining for prediction of air pollution. Two problems in such prediction are important: the generation and selection of the prognostic features, and final prognosis of the pollution level for the next day on the basis of the data of the previous day. In this paper we analyze and compare two methods of feature selection. One applies the genetic algorithm, and the second the linear method of stepwise fit. On the basis of such analysis we are able to select the most important features influencing the prediction. As a mathematical tool for final prediction we apply the neural networks. Three different solutions will be compared: the multilayer perceptron (MLP), radial basis function (RBF) network and support vector machine (SVM). |
Rights: | © University of West Bohemia |
Appears in Collections: | ISTET 2013 ISTET 2013 ISTET 2013 |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/11487
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