Abstract
In this paper a methodology for analyzing the behavior of the environmental noise pollution is proposed. It consists of a mobile application called ‘NoiseMonitor’, which senses the environmental noise with the microphone sensor available in the mobile device. The georeferenced noise data constitute Volunteered Geographic Information that compose a large geospatial database of urban information of the Mexico City. In addition, a Web-GIS is proposed in order to make spatio-temporal analysis based on a prediction model, applying Machine Learning techniques to generate acoustic noise mapping with contextual information.According to the obtained results, a comparison between support vector machines and artificial neural networks were performed in order to evaluate the model and the behavior of the sensed data.
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Acknowledgments
This work was partially sponsored by the Instituto Politécnico Nacional (IPN), the Consejo Nacional de Ciencia y Tecnología (CONACYT), and the Secretaría de Investigación y Posgrado (SIP) under grant 20162006.
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Torres-Ruiz, M., Juárez-Hipólito, J.H., Lytras, M.D., Moreno-Ibarra, M. (2016). Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_7
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DOI: https://doi.org/10.1007/978-3-319-42089-9_7
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