为提升混凝土坝变形预测的精度,采用具有出色的非线性数据挖掘能力与时间序列长、短期预测性能的长短期记忆网络(LSTM),提出了基于LSTM 网络的混凝土坝变形预测模型。实例分析表明,相比于常用的逐步回归、多元回归等方法,基于LSTM 网络构建的变形预测模型可有效挖掘大坝变形与影响因子间复杂的非线性关系,模型的建模与预测精度均得以显著提升。
To improve the prediction accuracy of concrete dam deformation, a Long Short-Term Memory(LSTM) network-based concrete dam deformation prediction model is proposed, which hasthe merits of excellent nonlinear data mining ability and the long and short-term prediction performance of time series.Example analysis shows that, compared with the commonlyused stepwise regression and multiple regression methods, the LSTM network-based deformation prediction model can effectively mine the complex nonlinear relationship between dam deformation and influencing factors. The modeling and predicting accuracy of the model can be significantly improved, providinga new method for dam deformation prediction.
欧斌,吴邦彬,袁杰,等.基于LSTM的混凝土坝变形预测模型[J].水利水电科技进展,2022,42(1):21-26.(OU Bin, WU Bangbin, YUAN Jie, et al. LSTM-based deformation prediction model of concrete dams[J]. Advances in Science and Technology of Water Resources,2022,42(1):21-26.(in Chinese))复制