基于ARIMA-BP组合模型的铁路路基冻害预测技术研究

Research on Frost Damage Prediction of Railway Subgrade Based on ARIMA-BP Combo Model

铁路路基沉降数据易受各种复杂因素的影响,为降低人工检修成本,提高预测精度,提出一种ARIMA-BP的组合预测模型。首先深入分析季节性冻土沉降变化规律及其数据特性,分别构建基于ARIMA(差分自回归滑动平均)模型和BP神经网模型的路基冻害预测模型,分析发现,单一ARIMA模型对短期线性沉降变化具有较强的预测性能,但在长期非线性变化沉降预测中表现较差;BP神经网络根据误差反向更新模型权重,可以深度挖掘序列的长期非线性变化趋势。故提出一种ARIMA-BP组合模型,利用BP神经网络完成初步预测并拟合出原始序列的残差序列,再采用ARIMA模型进行残差预测,将两者预测结果组合起来得到组合预测结果。实验表明,提出的组合模型RMSE较ARIMA预测方法降低76.9%,较BP神经网络预测方法降低58.6%,较基于ARIMA-BP最优权重组合预测方法降低45.9%,证明该方法能更好地表现路基沉降变化趋势,为铁路行车安全发挥预警作用。

Railway roadbed settlement data are susceptible to various complex factors.In order to reduce the cost of manual overhaul and improve the prediction accuracy,a combination of ARIMA-BP prediction model was proposed.First of all,in-depth analysis was done for seasonal permafrost settlement change rules and its data characteristics respectively,to construct the roadbed freezing prediction model based on ARIMA(differential autoregressive sliding average)model and BP neural network model.The analysis found that a single ARIMA model has a strong prediction performance for short-term linear settlement changes,but performs poorly in long-term nonlinear changes in the settlement prediction.BP neural network according to error inverse updated model weights,which deeply excavated the long-term nonlinear change trend of the sequence.Therefore,a combined ARIMA-BP model was proposed,which used BP neural network to complete the initial prediction and fit the residual sequence of the original sequence,and then adopted ARIMA model for residual prediction,and combined the two prediction results to obtain the combined prediction results.Experiments show that,the RMSE of the proposed combined model is 76.9%lower than that of ARIMA prediction method,58.6%lower than that of BP neural network prediction method,and 45.9%lower than that of the combination prediction method based on the optimal weights of ARIMA-BP,which proves that the method can better express the trend of changes in the settlement of roadbed,and play a role of early warning for the safety of railway traffic.