| Due to large, low energy consumption, strong adaptability and international navigation of transportation in water, water transportation occupies very important position in national economic construction. At present, water transportation keeps fast development in our country.With the development of maritime trade , maritime accidents often take place because of more and more wartercraft, increasing flow of marine traffic, increasing density of voyage, these maritime accidents casued by serious loss of people's lives, property and environmental pollution.. So the research in forecast of maritime accidents has actual significance. A forecast of maritime accidents has many characteristics, such as various influent-factors, irregular, randomness, higher-dimension nonlinear, data missing and so on. So the forecast of maritime accidents is difficult problem.SVM(Support vector machine) is a new machine learning technique developed from the middle of 1990s by Vladimir Vapnik. Support vector machines are a very specific class of algorithms, characterized by the use of a maximal margin hyper-plane the theory of kernels, the absence of local minima, convex optimization the sparseness of the solution, Mercer's theorem and the capacity control obtained by acting on the margin. A large number of experiments have shown that support vector machine has not only simpler structure, but also better performance, especially its better generalization ability. As a new machine learning technique, SVM has simple topology, providing the global only optimal solution, good of the generalization ability, useful information extrction in unknown distribution of small sample, solution higher-dimension and nonlinear convex optimization of sample space.Using regression forecast exacts good effective. This paper applies SVM method to the forecast of maritime accidents.Base on above all, this paper did the following works:1. Research STL (Statistical Learning Theory) and the theory of SVM. It mainly introduces three core concepts of STL, which are VC dimension, minimizing the bound by minimizing hand structural risk minimization. It also elaborates the ideas, counting steps and optimize algorithm of support vector classification and regression.2. Research maritime accident's characteristics. According to its characteristics , the paper overall thorough analysis and research three factors about human factors, environmental factors and ship factors.3. Research exponential smoothing model, regression model, grey system model and SVM regression model. Further study applications the forecast of maritime accidents.4. Researching the forecast of maritime accidents by SVM regression model, investigate maritime accidents data's pretreatment, selection of SVM regression model's parameters, principle and method of maritime accidents data's characteristics. Optimize kernel function, slecet gaussian kernel function forecast of maritime accidents in Yangtze river's certain valley by time characteristic, accdent's species charateristic and accident levels. The forecast result is better performance.5.Using VC++ program to get the forecast system of maritime accidents, comparing experimental results proves that SVM regression model is optimum. Summary the GM(1,1) model, exponential smoothing model and SVM regression model characteristics. |
