Artificial Neuronal Networks To Predict The Emissions of Carbon Dioxide (CO2) Using A Multilayer Network With the Levenberg-marquadt Training Method

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Abstract: This research work is based exclusively on the application of artificial neural networks, aimed at predicting the CO2 pollution index. For the design of the ANN, a multilayer network of Backpropagation type has been created and the Levenberg-Marquardt method was used for its training. The neural network consists of three layers: input (Input), hidden (Hidden Layer) and output (Output); the architecture was generated with Matlab software. The model was validated with comparisons between real and forecasted values, with the interest of recognizing the trend of the index both in the short, medium and long term. Good quality results were obtained when the actual values and those predicted by the system were checked, demonstrating that it is a highly accepted model for prediction, favoring the planning processes.

WSEAS Transactions on Environment and Development, ISSN / E-ISSN: 1790-5079 / 2224-3496, Volume 15, 2019, Art. #39

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Erik F. Méndez Garcés, José Herrera, Gabriela M. Mafla Andrés F. Morocho Caiza, "Artificial Neuronal Networks To Predict The Emissions of Carbon Dioxide (CO2) Using A Multilayer Network With the Levenberg-marquadt Training Method," WSEAS Transactions on Environment and Development, vol. 15, pp. 346-355, 2019, DOI:
Erik F. Méndez Garcés, José Herrera, Gabriela M. Mafla Andrés F. Morocho Caiza. Artificial Neuronal Networks To Predict The Emissions of Carbon Dioxide (CO2) Using A Multilayer Network With the Levenberg-marquadt Training Method. WSEAS Transactions on Environment and Development. 2019;15:346-355.
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