Prediction of black carbon in marine engines and correlation analysis of model characteristics based on multiple machine learning algorithms

• Published in: Environmental science and pollution research international 2022-11, Vol.29 (52), p.78509-78525
• Main Authors: Sun, Ying, Lü, Lin, Cai, Yun-kai, Lee, Peng
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Black carbon; Carbon; Correlation analysis; Earth and Environmental Science; Ecological effects; Ecotoxicology; Emissions; Environment; Environmental Chemistry; Environmental Health; Environmental regulations; Environmental science; Intelligence; Learning algorithms; Machine learning; Marine engines; Model accuracy; Neural networks; Optimization; Prediction models; Research Article; Support vector machines; Swarm intelligence; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-022-20496-4

Ship black carbon emissions have caused great harm to ecological environment. In order to estimate the black carbon emissions, thereby reducing the cost of black carbon experiments, here, we introduced four machine learning algorithms which are lasso regression, support vector machine, extreme gradient boosting, and artificial neural network to predict ship black carbon emissions. The prediction models were established with using the datasets acquired from similar marine engines under various steady-state conditions. The results show that SVM, XGB, and ANN have higher prediction accuracy than lasso regression, and the adjusted R 2 of each model is 0.9810, 0.9850, 0.9885, and 0.6088. Although ANN shows the best prediction performance, it is inferior to SVM and XGB in terms of model stability and training cost. Then, in order to simplify the optimization process of hyperparameters and improve the prediction accuracy of the model at the same time, we use three different swarm intelligence algorithms to automatically optimize the hyperparameters of SVM and XGB. In addition, we applied mutual information to measure the correlation between the characteristics of the prediction models and black carbon concentration and found that the characteristics which related to in-cylinder combustion have a strong correlation with the black carbon concentration. The findings in this paper prove the feasibility of machine learning in ship black carbon emission prediction and could provide references for reducing ship black carbon emissions and the formulation of emission regulations.