基于改进遗传算法的Holt-Winters模型在采空沉陷预测中的应用

针对遗传算法存在的缺陷，提出了用小生境方法改进遗传算法。为了提高采空沉陷预测精度，借助Holt-Winters模型的预测功能，应用改进遗传算法求解和优化Holt-Winters模型组合参数，形成了改进遗传算法-Holt-Winters模型组合算法。将组合算法应用于长平高速公路采空区路段沉陷预测，计算表明：改进遗传算法弥补了传统遗传算法易早熟、局部寻优能力弱的缺陷；改进遗传算法-Holt-Winters模型组合算法克服了按梯度试算法搜索质量差和精度不高的缺点，输出稳定性好，预测结果相对误差在2%以内，预测精度显著提高；在采空沉陷中长期预测的相对误差小于0.79%，该算法可用于中长期采空沉陷预测。

Mining Subsidence Forecast Method Based on Improved Genetic Algorithm and Holt-Winters Model

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.