基于Kalman滤波的ARIMA-NAR神经网络模型

深基坑变形监测在城市建设安全施工中显得越来越重要.鉴于监测数据不可避免地存在噪声及单个预测模型存在的预测残差问题,为提高基坑监测预测精度,以兰州市某深基坑监测中具有明显沉降的ZJ52为例,采取一种基于Kalman去噪的ARIMA-NAR神经网络组合模型进行预测分析.结果发现,经Kalman去噪后建立的ARIMA-NAR组合模型的预测结果的平均绝对误差、平均相对误差和残差方差分别为0.43、0.04、2.23 mm,预测结果均优于单一的ARIMA和NAR神经网络模型的预测结果,预测精度较好,其结果可为本项目的安全施工提供可靠指导.

Application of ARIMA-NAR neural network model based on Kalman filter in deformation monitoring of deep foundation

Deformation monitoring of deep foundation pits is becoming more and more important in the security control of urban construction. Because of the unavoidable noise in the monitoring data and the prediction residual problem from the single prediction model, it is necessary to improve the prediction accuracy of deep foundation excavation. Taking ZJ52 of a deep foundation pit in Lanzhou as an example, and based on Kalman filtering, the paper used a combined model of ARIMA-NAR neural network to predict and analyze the change trend of the observation point. The results show that the average absolute error, average relative error and residual variance from the established model are 0.43 mm, 0.04 mm and 2.23 mm, respectively. The prediction results are better than those of single ARIMA and NAR neural network models. The prediction results from ARIMA-NAR neural network with Kalman filtering can provide reliable guidance for the security construction of this project.