| DEVELOPMENT OF A LIGHT AND ACCURATE NOX PREDICTION
MODEL FOR DIESEL ENGINES USING MACHINE LEARNING AND
XAI METHODS |
| Jeong Jun Park 1, Sangyul Lee 2, Seunghyup Shin 3,4, Minjae Kim 5, Jihwan Park 6 |
1Department of Electrical and Computer Engineering, Sungkyunkwan University
2Division of Mechanical and Electronics Engineering, Hansung University
3Artificial Intelligence Railroad Research Department, Korea Railroad Research Institute
4Department of Artificial Intelligence, Sejong University
5Department of Mechanical Engineering, Myongji University
6Department of Mechanical and Aerospace Engineering, Seoul National University |
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| ABSTRACT |
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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
R2 with the test data. By extracting the dominant features influencing NOx prediction, we could develop a lightweight and
accurate NOx prediction model. |
| Key Words:
Diesel engines, NOx prediction model, Machine learning, Random forest, XAI |
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Corresponding Author.
Sangyul Lee , Email. 
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