为了解决大坝变形单测点预测模型没有考虑测点间的空间位置关系、难以刻画大坝变形的整体响应特性以及基于回归分析的统计模型难以揭示环境量与效应量间复杂的非线性映射关系,预测精度不佳的问题,提出了一种基于小波理论、采用麻雀搜索算法(SSA)优化极限学习机(ELM)的大坝变形时空预测模型,并以某实际工程为例验证了模型的可行性。首先,采用小波分析剔除大坝原始位移测值中的噪声,接着从时间-空间两个维度出发考虑测点坐标变化对位移的影响,利用SSA-ELM对环境量与效应量进行非线性建模,进而构建了基于小波的SSA-ELM大坝变形时空预测模型。实例分析表明:所提模型能够准确预测出未布置测点部位的变形,其复相关系数为0.996 8、均方根误差为0.340 4、平均绝对误差为0.275 4,均明显高于ELM模型和统计模型。所提模型融合了时间和空间维度且预测精度高,对分析评估大坝安全具有重要参考价值。

A spatio-temporal prediction model for dam deformation is proposed, which incorporates the wavelet theory and the Sparrow Search Algorithm (SSA) to optimize the extreme learning machine (ELM). This model addresses the challenge of accurately describing the overall response characteristics of dam deformation using single measurement point prediction models which fail to account for the spatial relations among measure points. Additionally, it overcomes the limitations of statistical models based on regression analysis, which struggle to uncover the complex nonlinear mapping relationship between environmental variables and the magnitude of deformation, often resulting in poor prediction accuracy. To validate the feasibility of the proposed approach, an actual dam project is taken as an illustrative example. The approach begins with wavelet analysis to eliminate noise from the original displacement measurements of the dam. Subsequently, the influence of coordinate changes in the measurement points on displacement is considered. SSA-ELM is employed to establish non-linear models for independent and dependent variables, constructing a spatio-temporal prediction model for dam deformation based on wavelet analysis. Application of the proposed model to a real-world example demonstrates its ability to accurately predict deformation across non-arranged measuring points. The model exhibits impressive performance indicators, including a highly significant complex correlation coefficient of 0.996 8, a root mean square error of 0.340 4, and an average absolute error of 0.275 4, which exceed those achieved by both the ELM model and statistical model. By integrating both temporal and spatial dimensions, the proposed model achieves high prediction accuracy and holds significant value as a reference for the analysis and evaluation of dam safety.