Title :
Application Research of Support Vector Regression in Coal Mine Ground-Water-Level Forecasting
Author :
Taian, Liu ; Xin, Xue ; Xinying, Liu ; Huiqi, Zhao
Author_Institution :
Dept. of Inf. & Eng., Shandong Univ. of Sci. & Technol.(SDUST), Taian, China
Abstract :
The forecast of the mine Ground-water-level is an issue with many influencing factors, highly non-linear and temporal series. SVR (Support Vector Regression) is applied to forecast Coal Mine Ground-water-level in this paper. Appropriate kernel function and parameters are chosen based on the analysis to SVR regression algorithm. This paper proposes the Forecasting Model of Coal Mine Ground-water-level basing on SVR regression algorithm and determines the forecast of the input factor and the output factor according to the physical geography and the hydrology geology situation of the chosen mining area. The numerical test results show that the forecast results have compatibility with the actual measurement result. We verify that the forecast model of Coal Mine Ground-water-level has effect, and provide a new effective method to the Forecasting of Coal Mine Ground-water-level.
Keywords :
coal; mining industry; regression analysis; support vector machines; coal mine ground water level forecasting; geography; hydrology geology; kernel function; mining area; support vector regression; Function approximation; Ground support; Industrial accidents; Information technology; Kernel; Mining industry; Predictive models; Support vector machine classification; Support vector machines; Technology forecasting; coal mine ground-water-level; cross validation methods; forecasting model; kernel function; support vector regression algorithm;
Conference_Titel :
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3600-2
DOI :
10.1109/IFITA.2009.61
