Research On The Application Of Support Vector Machine In Oil Spill Fluorescence Spectrum Analysis

With the development and use of marine resources, sea is suffered serious pollution, and oil pollution is especially obvious. Oil spills often cause extensive sea pollution and tremendous harm. Therefore, real-time and correct monitoring oil spill is great significance.The paper carefully compares the domestic and foreign oil spills identification method, and analyses the reason of previous neural network laser fluorescence spectrum identification with slower speed and lower accuracy rate. The paper advances that the shape of the fluorescence spectral signatures is the crucial character of identification oil spills, and introduces the Support Vector Machine (SVM) which is based on the statistical learning theory, to identify the types of fluorescence spectrum.The paper chooses the abroad application neural network model for back-propagation (BP) network and Radial Basis Function (RBF) network in many of the artifiacal neural network models. The core work of the paper is the establishment of BP, RBF networks and SVM laser fluorescence spectrum identification model. The use of laser remote sensing equipment obtained spectrums to test the several establishment model and obtain the results of the experiments. The main research of experimental testing is the identification speed and identification accuracy of three models .Testing experiments, the average training time of BP network laser fluorescence spectrum identification model is 16.0280s, with an accuracy rate of 86.7%. The average training time of RBF network laser fluorescence spectrum to identification model is 0.6064s, with an accuracy rate of 86.7%.The average training time of SVM laser fluorescence spectrum identification model is 0.0184s, with an accuracy rate of 96.7%. RBF network and SVM identification model spend less time training. BP network identification model of training to be time-consuming obviously much bigger. The accuracy of BP and RBF network identification model is the same, and the accuracy of SVM identification model is the best. The results show that performance of SVM laser fluorescence spectrum identification model is the best. It is a very promising approach.