Forecasting of CO2 Emission using LSTM- based Deep Learning Model

Formulation of Knowledge and future predictions of carbon. The transportation industry is a source of dioxide (CO2) emissions [20]. An important step to manage the issue of climate change. This paper contemplates a hybrid method of modeling that is a combination of. Conventional regression, machine learning, deep learning, and metaheuristic optimization to forecast CO2 emission concerning vehicles. Key analysis is done through structured training and testing. Car specifications and fuel efficiency information. Various linear [18] and The accuracy of the models characterizes the non-linear models. Their validity and usefulness. Deep learning models are more efficient in representing emission patterns of more complexity but Optimization approaches increase the general performance. The initiative following estimates of emissions are acceptable good decision making in the transportation planning. Index Termstransportation, predictive modeling, CO2, emission, and optimization, deep learning.