A Hybrid Intelligent Algorithm for Improved Prediction of Annual CO2 Emission

This study presents a hybrid meta-modeling algorithm for carbon dioxide (CO2) emission estimation. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). Different FLR models are considered to cover the latest algorithms and viewpoints. ANN with different training algorithms and transfer functions is also applied to data sets. The proposed hybrid algorithm uses analysis of variance (ANOVA), and mean absolute percentage error (MAPE) to select between ANN, FLR or conventional regression for future CO2 emission estimation. The intelligent algorithm of this study is then applied to estimate CO2 emission in seven countries including India, Canada, Brazil, France, Japan, United Kingdom and United State. Different models are selected as preferred model for annual CO2 emission estimation in these countries. Whereas the preferred model for CO2 emission estimation in Japan, Canada and France is ANN, the preferred model for India, Brazil, United Kingdom and United State is selected as FLR. This shows how adopting the proposed hybrid algorithm could help in selecting the preferred model between FLR, ANN and CR in order to cover possible noise, complexity and ambiguity.