Application Of Support Vector Machines In Forecast Of Coal Mine Underground Water Table

Due to heavy coal exploitation in recent years, many serious accidents about underground water of coal mines frequently happened, which jeopardized to people's lives and wealth. So the research in forecast of coal mine underground water table has actual significance. A forecast of coal mine underground water table has many characteristics, such as various influent-factors, higher-dimension nonlinear, time series and so on. This paper applies Support Vector Machine (SVM) method to the forecast of coal mine underground water table.SVM 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.Firstly, the paper states the theory basis of SVM: Statistical Learning Theory (STL). It mainly introduces three core concepts of STL, which are VC dimension, minimizing the bound by minimizing h and structural risk minimization. It also elaborates the ideas, counting steps and optimize algorithm of support vector classification and regression.Secondly, after it analyzes and compares several techniques of SVM, it ascertains the suitable techniques required in the forecast of coal mine underground water table, and explains the necessary theory to them.Finally, this paper establishes a model during the course of the forecast by using SVM, and analyzes the influence model parameters to model performance. According to the...