| 摘要: |
| 针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题，视大坝变形观测资料为非平稳时间序列，从影响大坝变形的因素出发，将其分为周期性影响因素与随机影响因素，利用多尺度小波分析方法将大坝变形监测序列分解并重构，结合BP神经网络与自回归积分滑动平均模型（Autoregressive Integrated Moving Average Model，简记ARIMA）对其随机信号与系统信号分项训练预报，并将其预报值相叠加，据此，应用时间序列原理提出了一种基于BP-ARIMA的混凝土坝多尺度变形组合预报模型。工程实例分析表明，所建组合模型较常规模型能够有效挖掘监测信息中所蕴含的有效成分，预报精度显著提升，且计算分析过程简便，为高边坡及水工建筑物中其他监测指标的预报提供了新方法。 |
| 关键词: 混凝土坝 变形预报 小波分析 BP神经网络 ARIMA模型 |
| DOI： |
| 分类号:TV642 |
| 基金项目:国家自然科学基金资助项目（51409139，51569014，51669013） |
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| Multi-scale deformation combination forecast model for concrete dam based on BP-ARIMA |
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WEI Bowen, YUAN Dongyang, CAI Lei, WEN Yongbing, XU Zhenkai
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School of Civil Engineering and Architecture, Nanchang University, Nanchang
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| Abstract: |
| In conventional dam deformation monitoring models, information mining of dam prototype observation data is limited and forecast precision is not up to standard. Dam deformation prototype data can be regarded as non-stationary time series, and considering the influence factors of dam deformation， it can be decomposed into cyclical factors and random factors. Dam deformation monitoring data are decomposed and reconstructed by multi-scale wavelet analysis method in this paper, BP neural network and Autoregressive Integrated Moving Average Model (ARIMA) are separately used to analyze and forecast the random signal and system signal contained in deformation monitoring data, and the forecast values based on the two models are superimposed, and the multi-scale deformation combination forecast value for concrete dam based on BP-ARIMA is proposed according to the time series principle. Example shows that, compared with the conventional models, active components contained in the monitoring data are effectively excavated, and the forecast precision is improved obviously, meanwhile, the calculation and analysis process is simple in the proposed combination model. A new method of the deformation forecast for high slope and other hydraulic structures is presented. |
| Key words: concrete dam deformation forecast wavelet analysis BP neural network ARIMA model |
