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《Journal of Jilin University(Earth Science Edition)》 2013-02
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Mining Subsidence Forecast Method Based on Improved Genetic Algorithm and Holt-Winters Model

Peng Shuaiying1,Li Guangjie1,Peng Wen2,Ma Jianquan1,Wang Xuedong1,Qin Shengwu1 1.College of Construction and Engineering,Jilin University,Changchun 130026,China 2.China Railway Erju Group Corporation Survey and Design Institute Co.,Ltd,Chengdu 610036,China  
In order to improve the prediction accuracy of mining subsidence,a method based on improved genetic algorithm and Holt-Winters model is proposed.Improved genetic algorithm(IGA) is put forward due to the defects of genetic algorithm.With the aid of the improved genetic algorithm,parameters of Holt-Winters model can be greatly optimized.Then the IGA-Holt-Winters model is applied in a mining subsidence forecast of Changchun-Siping highway.The result shows that the improved genetic algorithm enhances convergence speed and precision of the algorithm.Furthermore,the convergence of method improve forecast accuracy with percentage error less than 2% and mean errors less than 0.79% for long-term prediction of the mining subsidence.The model has better prediction accuracy and can be used for long-term prediction of mining subsidence.
【Fund】: 国家自然科学基金青年基金项目(41202197)
【CateGory Index】: TP18;TD325
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