Explainable Deep Learning Model for Carbon Dioxide Estimation

抄録

In recent years, environmental sustainability and the reduction of CO₂ emissions have become significant research topics. To effectively reduce CO₂ emissions, recent studies have used deep learning models to provide precise estimates, but these models often lack interpretability. In light of this, our study employs an explainable neural network to learn fuel consumption, which is then converted to CO₂ emissions. The explainable neural network includes an explainable layer that can explain the importance of each input variable. Through this layer, the study can elucidate the impact of different speeds on fuel consumption and CO₂ emissions. Validated with real fleet data, our study demonstrates an impressive mean absolute percentage error (MAPE) of only 3.3%, outperforming recent research methods.