Application of Deep Learning Initiatives for CO2 Emissions Forecasting

Abstract

This work models and forecasts vehicle CO2 emissions, a major source of atmospheric changes and climate disruptions, using cutting-edge artificial intelligence. The CO2 emission by vehicle dataset from Kaggle, which includes several features such vehicle class, engine size, cylinder transmission, fuel type, fuel consumption, city, highway, comb, and CO2 emissions, was used to build the model. To predict CO2 emissions, a hybrid model (CNN-LSTM-MLP) was developed based on long short-term memory network (LSTM), convolution neural network (CNN), and multi-layer perceptron (MLP). The proposed model shows superior results compared with CNN, MLP, LSTM, Light Gradient Boosting Machine (LGBM) Regressor, support vector machine (SVM), Linear Regression, and Random Forest.