Development of a Light and Accurate Nox Prediction Model for Diesel Engines Using Machine Learning and Xai Methods

In the previous research, we had developed a Nitrogen Oxides emissions (NOx) prediction model using deep learning methodology. However, it is necessary to develop a lightweight model for practical application. We developed a machine learning model for predicting NOx using Random Forest method to reduce the negligible input features. For comparison, a Base model was developed with all features. The Shapley Additive Explanations method, which can show the influence of the input features, and the Pearson Correlation Coefficient method, which can show the proportional relationship between the output and the input values, were used to choose the dominant features. The final model was determined using the Shapley Additive Explanations method. The final model shows similar prediction performance to the base model, though it has only 11 features (30 % of the Base model). The final model shows 15.76 for the root mean square error and 0.965 for R 2 with the test data. By extracting the dominant features influencing NOx prediction, we could develop a lightweight and accurate NOx prediction model.