CO_(2) emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities.Consequently,these factors must be considered for CO_(2) emission predicti...CO_(2) emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities.Consequently,these factors must be considered for CO_(2) emission prediction for seven middle eastern countries including Iran,Kuwait,United Arab Emirates,Turkey,Saudi Arabia,Iraq and Qatar.In order to propose a predictive model,a Multilayer Perceptron Artificial Neural Network(MLP ANN)is applied.Three transfer functions including logsig,tansig and radial basis functions are utilized in the hidden layer of the network.Moreover,various numbers of neurons are applied in the structure of the models.It is revealed that using MLP ANN makes it possible to accurately predict CO_(2) emission of these countries.In addition,it is concluded that using logsig transfer function leads to the highest accuracy with minimum value of mean squared error(MSE)which is followed by the networks with radial basis and tansig transfer functions.The R-squared of the networks with logsig,radial basis and tansig transfer functions are 0.9998,0.9997 and 0.9996,respectively.Finally,comparison of the proposed model with a similar study,considered five countries in the same region,reveals higher accuracy in term of MSE.展开更多
基金This work was supported by College of Engineering and Technology,the American University of the Middle East,Kuwait.Homepage:https://www.aum.edu.kw.
文摘CO_(2) emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities.Consequently,these factors must be considered for CO_(2) emission prediction for seven middle eastern countries including Iran,Kuwait,United Arab Emirates,Turkey,Saudi Arabia,Iraq and Qatar.In order to propose a predictive model,a Multilayer Perceptron Artificial Neural Network(MLP ANN)is applied.Three transfer functions including logsig,tansig and radial basis functions are utilized in the hidden layer of the network.Moreover,various numbers of neurons are applied in the structure of the models.It is revealed that using MLP ANN makes it possible to accurately predict CO_(2) emission of these countries.In addition,it is concluded that using logsig transfer function leads to the highest accuracy with minimum value of mean squared error(MSE)which is followed by the networks with radial basis and tansig transfer functions.The R-squared of the networks with logsig,radial basis and tansig transfer functions are 0.9998,0.9997 and 0.9996,respectively.Finally,comparison of the proposed model with a similar study,considered five countries in the same region,reveals higher accuracy in term of MSE.
