Study on the Detection Method of Soil-Motor Oil Contamination Combined with Genetic Algorithm Spectral Wavelength Selection

Abstract

To classify and detect the type and content of petroleum hydrocarbon contaminants in the soil surface layer, fluorescence spectrometry is commonly used. The experimental oils were selected from three common engine oils available in the market: Loxson L-CKC220 gear oil, APSIN 10W-40 engine oil and Jaguar 200 SF MA 15W-40 motorcycle oil. The fluorescence spectra of the oils were obtained using the fluorescence-induced technique, the spectral wavelengths were selected using a genetic algorithm (GA), and the detection models were constructed by combining RF (Random Forest), AdaBoost, and Gradient Enhanced Decision Tree (GBDT) regression algorithms for classification, identification, and concentration prediction analysis. The experimental results show that the average accuracy of classification and identification of gear oil, engine oil and motorcycle oil reach 83.9, 97.8, and 92.2%, respectively. Comparative analysis of the prediction results of the three concentration regression models shows that while all algorithms have high model prediction accuracy, GA combined with GBDT regression model has the best prediction performance for gear oils, engine oils and motorcycle oils, and improves the prediction accuracies by 62.7, 42.3, and 48.3% compared to the prediction accuracies of the wavelength selection without the use of GA, respectively. In summary, GA-based spectral wavelength selection combined with machine learning algorithms has high prediction accuracy and precision for the classification and prediction of motor oil contaminants in selected specific soils, and can be used as an effective detection method.