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Application of Machine Learning Techniques in the Study of the Relevance of Environmental Factors in Prediction of Tropospheric Ozone
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Author(s): Juan Gómez-Sanchis (University of Valencia, Spain), Emilio Soria-Olivas (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Blasco (Centro de AgroIngeniería, IVIA, Spain), Juan Guerrero (University of Valencia, Spain)and Secundino del Valle-Tascón (University of Valencia, Spain)
Copyright: 2010
Pages: 15
Source title:
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
Source Author(s)/Editor(s): Marcos Gestal Pose (University of A Coruna, Spain)and Daniel Rivero Cebrián (University of A Coruna, Spain)
DOI: 10.4018/978-1-61520-893-7.ch017
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Abstract
This work presents a new approach for one of the main problems in the analysis of atmospheric phenomena, the prediction of atmospheric concentrations of different elements. The proposed methodology is more efficient than other classical approaches and is used in this work to predict tropospheric ozone concentration. The relevance of this problem stems from the fact that excessive ozone concentrations may cause several problems related to public health. Previous research by the authors of this work has shown that the classical approach to this problem (linear models) does not achieve satisfactory results in tropospheric ozone concentration prediction. The authors’ approach is based on Machine Learning (ML) techniques, which include algorithms related to neural networks, fuzzy systems and advanced statistical techniques for data processing. In this work, the authors focus on one of the main ML techniques, namely, neural networks. These models demonstrate their suitability for this problem both in terms of prediction accuracy and information extraction.
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