摘要
现有的高拱坝施工仿真参数更新研究多是单独进行概率预测或考虑时序特性进行点预测,难以在考虑参数的时序特征的同时对其随机性进行定量描述。针对此问题,本研究利用差分整合移动平均自回归(ARIMA)模型可进行考虑时序特征的概率预测,且长短时记忆网络(LSTM)模型可以学习参数时序复杂非线性特征的优势,提出基于ARIMA-LSTM的高拱坝施工仿真参数更新模型。该模型通过ARIMA模型进行参数时序线性部分预测,并利用LSTM模型对ARIMA模型输出的残差进行训练预测,将ARIMA模型得到的线性预测结果和LSTM模型预测得到的残差非线性结果融合,再进行95%置信区间的概率预测得到最终结果,实现高拱坝施工仿真参数在考虑参数的时序特征的同时对其随机性进行描述。通过与ARIMA、ARIMA-BP、随机森林(RF)模型进行对比,本文所提出的方法具有较高精度(MSE为0.518、MAE为0.519、RMSE为0.720),将预测得到的施工仿真参数输入到高拱坝施工系统中进行仿真计算,得到仿真结果比传统仿真精度有较大提升。
Most previous studies for updating the simulation parameters of high arch dam construction are based on probability prediction alone or point-wise prediction considering their time series characteristics;such methods are usually faced with difficulty in quantitative description of their randomness while considering their time series characteristics.To couple both factors,this study uses an Autoregressive Integrated Moving Average model(ARIMA)to predict the probability along with consideration of parameter time series characteristics,and develops a new intelligent updating model of the simulation parameters of high arch dam construction based on ARIMA and a Long Short-Term Memory model(LSTM)that can learn the advantages of complex nonlinear characteristics of parameter time series.This new model uses ARIMA to predict the linear part of the parameter time series,and uses LSTM to train and predict the residuals output by the ARIMA model.We fuse the predicted linear part and the nonlinear part of the predicted residuals,and then make probability predictions at the 95%confidence interval to obtain the final result.Thus,we can calculate the construction simulation parameters that describe both randomness and temporal characteristics,and have achieved a new method of higher accuracy(with MSE of 0.518,MAE of 0.519 and RMSE of 0.720)than the model of ARIMA,ARIMA-BP neural network,or random forest(RF).Compared with traditional simulations,the simulation results of a high arch dam construction system are greatly improved using the simulation parameters predicted by our new method.
作者
关涛
陈普瑞
于浩
GUAN Tao;CHEN Purui;YU Hao(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China)
出处
《水力发电学报》
CSCD
北大核心
2023年第11期146-156,共11页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51839007)。
