水电能源科学

渡槽是引调水工程中常见的输水建筑物，准确预测渡槽变形对于确保水利工程稳定运行至关重要。为此，以南水北调工程中的潦河渡槽为例，基于渡槽长期变形监测数据建立弹性网回归、多元线性回归、逐步回归、岭回归和LASSO回归5种不同线性可加模型，并采用5种不同线性可加模型对渡槽变形行为的预测结果进行比较。结果表明，随着预测时间增加，不同线性可加模型预测精度均呈现逐渐下降的趋势；LASSO模型通过交叉验证选择最优正则化参数，实现变量选择精简化，模型复杂度降至最低；同时验证了训练长度会对多元线性回归和逐步回归的预测效果产生影响。研究结果可为选择渡槽变形预测模型提供一些有益的参考。

Aqueducts are common water conveyance structures in water diversion projects, and accurate prediction of aqueduct deformation is crucial for ensuring the stable operation of water conservancy projects. For this purpose, taking the Liaohe Aqueduct in the South-to-North Water Diversion Project as an example, five different linear additive models, namely elastic net regression, multiple linear regression, stepwise regression, ridge regression and LASSO regression, were established based on the long-term deformation monitoring data of the aqueduct. The prediction results of the aqueduct's deformation behavior by the five different linear additive models were compared. The results indicate that as the prediction time increases, the prediction accuracy of different linear additive models gradually decreases. The LASSO model selects the optimal regularization parameter through cross-validation, achieving variable selection simplification and minimizing model complexity. Additionally, it is verified that the training length affects the prediction performance of multiple linear regression and stepwise regression. The findings of this study provide valuable references for selecting prediction model of aqueduct deformation.