Prediction of air pollution generated by CO2 emissions in Peru using ARIMA and Neural Networks methods
DOI:
https://doi.org/10.53673/jb.v1i1.4Keywords:
Modelo ARIMA, Redes Neuronales, GEI, CO2Abstract
Carbon dioxide is the main greenhouse gas (GHG) that leads to global warming and, consequently, climate and environmental change. It brings negative effects to economic development, human life and the environment. It is extremely important to be able to accurately measure and predict the emission of carbon dioxide, since in this way we can carry out a good sustainable policy for our environment. Given this, the present investigation has as its main objective to find the best model for the prediction of carbon dioxide (CO2) emissions in Peru, through the comparative evaluation of the ARIMA and Artificial Neural Networks methods. Annual data on CO2 emissions from the World Bank were used, which were analyzed by programming the free software R studio. To determine the best model, forecast errors such as: root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used. The results reveal that the most appropriate model between these two methods for the prediction of CO2 emissions in Peru is the ANN(5-10-1) neural network, that is, the neural network with five lagging values as input connected by ten. nodes in the hidden layer and a single output layer, which had a higher precision with RMSE = 1125.82, MAE = 1040.68 and MAPE = 1.90 in the test phase compared to the best ARIMA(0,1,10) model. which had a RMSE = 4223.73, MAE = 3143.40 and MAPE = 5.80 in the testing phase. In conclusion, neural networks can be used to predict CO2 emissions, which clearly showed that annual CO2 emissions in Peru will increase in the coming years. These real insights will be useful for policy makers to bring significant changes in the main environmental areas in our country.
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