Deep or Shallow? A Comparative Analysis on the Oil Species Identification Based on Excitation-Emission Matrix and Multiple Machine Learning Algorithms.

Abstract

With the continuous expansion of petroleum extraction, transportation, and storage, the risk of oil spills at sea has also increased, posing a serious threat to marine safety. The excitation-emission matrix (EEM), which is composed of the fluorometric spectra under multiple excitation wavelengths, becomes a feasible approach to identify oil species. Despite the fact that various machine learning models have been applied to analyse EEMs of oil pollutants, it is unclear how much improvements the deep learning models have achieved, especially comparing with the shallow learning models. This paper presents a comparative analysis on the oil species identification using four types of machine learning models: random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and deep convolutional neural network (DCNN). The fluorescence of some common oils was excited using a tuneable xenon lamp and collected with a high-resolution spectrometer to form the EEMs for model training and testing.The results show that SVM, BPNN, and DCNN achieved high identification accuracies that are more than 93% on all types of oils tested in the study. The two deep learning models didn't have significant improvement over the SVM model. Considering the fact that the deep learning models require much larger number of calculations and longer running time, the SVM tends to be more suitable for oil species identification when considering the balance between the model accuracy and efficiency. This study provides some guidance on the choices of oil species identification model in the cases of oil spills.