Sensing and Forecasting of Pollution Data in Mexico City
Elías-J. Ventura-Molina1, Raúl Jiménez-Cruz2, Adolfo Rangel-Díaz-de-la-Vega3
1Elías-J. Ventura-Molina*, CIC, Instituto Politécnico Nacional, CDMX, México.
2Raúl Jiménez-Cruz, CIC, Instituto Politécnico Nacional, CDMX, México.
3Adolfo Rangel-Díaz-de-la-Vega, CIC, Instituto Politécnico Nacional, CDMX, México.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2372-2378 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A2247109119/2019©BEIESP | DOI: 10.35940/ijeat.A2247.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we present the characteristics of sensors used to monitor the pollution levels in Mexico City, namely sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3), , and carbon monoxide (CO). A novel algorithm to predict contamination levels is presented: the Gamma classifier. Also, a new coding technique is introduced, allowing the conversion from a series of values taken from SIMAT databases into a set of patterns, which in turn are useful for the task of pollutant forecasting. Experimental results show a competitive performance by the Gamma classifier as a predictor, when compared to other methods.
Keywords: Associative Memory, Pattern Classifiers, Pollutant Forecasting; Pollutant Sensing.