| 利用SVM与ARIMA组合模型进行大坝变形预测 |
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| 引用本文： | 杨恒,岳建平,周钦坤.利用SVM与ARIMA组合模型进行大坝变形预测[J].测绘通报,2021,0(4):74-78. |
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| 作者姓名： | 杨恒 岳建平 周钦坤 |
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| 作者单位： | 河海大学地球科学与工程学院, 江苏 南京 211100 |
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| 基金项目： | 国家重点研发计划（2018YFC1508603） |
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| 摘 要： | 由于大坝位移时间序列数据受各种复杂因素的影响，具有非平稳和非线性等特征，因此，利用传统、单一的时间序列预测模型较难准确地描述大坝位移变形的复杂规律。综合考虑大坝位移时间序列非线性和线性特征，本文提出了一种SVM和ARIMA相结合的时间序列预测模型。将大坝变形的时间序列分为非线性部分和线性部分。针对非线性部分，利用SVM进行滚动预测，并与NAR动态神经网络进行对比，试验表明SVM处理非线性问题具有相对的优势；针对线性部分，通过ARIMA模型对其进行单步滚动预测，综合两项预测结果得到组合模型的预测值。结合大坝实测资料对组合模型进行检验，试验结果表明，SVM-ARIMA组合模型的预测精度高，能更好地描述大坝位移的变化趋势，具有一定的实用价值。
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| 关 键 词： | 大坝变形 SVM ARIMA模型 SVM-ARIMA组合模型 滚动预测 |
| 收稿时间： | 2020-05-07 |
Dam deformation prediction using SVM and ARIMA combined model |
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| YANG Heng,YUE Jianping,ZHOU Qinkun.Dam deformation prediction using SVM and ARIMA combined model[J].Bulletin of Surveying and Mapping,2021,0(4):74-78. |
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| Authors: | YANG Heng YUE Jianping ZHOU Qinkun |
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| Institution: | College of Earth Science and Engineering, Hohai University, Nanjing 211100, China |
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| Abstract: | Because the time series data of dam displacement is affected by various complex factors and has non-stationary and nonlinear characteristics,it is difficult to accurately describe the complex laws of dam displacement and deformation using a single traditional time series prediction model. Considering of the comprehensive consideration of the nonlinear and linear characteristics of the dam displacement time series,this paper proposes a time series prediction model based on the combination of SVM and ARIMA. The dam deformation time series is divided into the nonlinear part and linear part. For the nonlinear part,the support vector machine is used for rolling prediction. Compared with the NAR dynamic neural network,the support vector machine has a relative advantage in dealing with nonlinear problems. For the linear part,a single-step rolling prediction is made through the ARIMA model,and the prediction value of the combined model is obtained by combining the two prediction results. The combined model is tested with the measured data of the dam. The experimental results show that the SVM-ARIMA combined model has high prediction accuracy and can better describe the change trend of the dam displacement, which has certain practical value. |
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| Keywords: | dam deformation SVM ARIMA model SVM-ARIMA combined model rolling prediction |
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