准确预测的入库日径流在水库优化调度中发挥着重要作用.为提高预测精度,提出一种基于小波包变换(WPT)和蜣螂优化(DBO)算法、珍鲹优化(GTO)算法、泥环算法(MRA)优化正则化极限学习机(RELM)的预测模型,并将其应用于三峡水库入库日径流预测研究.首先,利用WPT将三峡水库入库日径流时间序列分解为1个周期项分量和1个波动项分量;其次,利用DBO、GTO、MRA分别优化RELM输入层权值和隐含层偏差,建立WPT-DBO-RELM、WPT-GTO-RELM、WPT-MRA-RELM模型;最后,利用所建立的3种模型分别对入库日径流周期项分量和波动项分量进行预测和重构,并构建基于极限学习机(ELM)的WPT-DBO-ELM、WPT-GTO-ELM、WPT-MRA-ELM模型、基于 BP 神经网络的 WPT-DBO-BP、WPT-GTO-BP、WPT-MRA-BP 模型、未经优化的 WPT-RELM、WPT-ELM、WPT-BP模型和未经分解的DBO-RELM、GTO-RELM、MRA-RELM模型作对比分析模型.结果表明:①WPT-DBO-RELM、WPT-GTO-RELM、WPT-MRA-RELM模型对三峡水库入库日径流预测的平均绝对百分比误差MAPE分别为0.512％、0.519％、0.762％,平均绝对误差 MAE 分别为54.05、55.97、86.76 m3/s,均方根误差 RMSE 分别为84.99、84.81、128.18 m3/s,决定系数DC≥0.999 4,希尔不等系数TIC≤0.005 17,预测效果优于其他12种模型,具有更高的预测精度和更好的泛化能力.②DBO、GTO、MRA能有效优化RELM网络参数,显著提高RELM预测性能.③引入正则化项的RELM可有效防止预测模型过拟合,提高模型的泛化能力,预测性能优于ELM、BP网络.④所构建的3种模型预测精度高、计算规模小,是一种有效的入库日径流时间序列预测模型.
Prediction of Daily Inflow Runoff of Three Gorges Reservoir Using Regularized Extreme Learning Machine Optimized by Three New Swarm Intelligent Algorithms
Accurate prediction of daily inflow runoff is crucial for optimizing reservoir operation.To enhance the pre-cision of daily inflow runoff forecasting,a prediction model integrating Wavelet Packet Transform(WPT),Dung Beetle Optimizer(DBO),Giant Trevally Optimizer(GTO),and Mud Ring Algorithm(MRA)optimized Random-ized Extreme Learning Machine(RELM)is proposed and applied to forecasting daily inflow runoff in the Three Gorges Reservoir.Initially,WPT is utilized to decompose the daily runoff time series into a periodic term compo-nent and a fluctuation term component.Subsequently,by employing DBO,GTO,and MRA to optimize the input layer weights and hidden layer bias of RELM,the WPT-DBO-RELM,WPT-GTO-RELM,and WPT-MRA-RELM models are established.These models are then employed to predict and reconstruct the periodic and fluctuation com-ponents of daily inflow runoff.Comparative models such as WPT-DBO-ELM,WPT-GTO-ELM,and WPT-MRA-ELM based on Extreme Learning Machine(ELM),as well as WPT-DBO-BP,WPT-GTO-BP,and WPT-MRA-BP based on BP neural network,along with unoptimized WPT-RELM,WPT-ELM,and WPT-BP models and unde-composed DBO-RELM,GTO-RELM,and MRA-RELM models are utilized for analysis.Results indicate that:1)The mean absolute percentage error(MAPE)of WPT-DBO-RELM,WPT-GTO-RELM,and WPT-MRA-RELM models for predicting the daily inflow in Three Gorges Reservoir is 0.512％,0.519％,and 0.762％respectively,with Mean Absolute Error(MAE)of 54.05 m3/s,55.97 m3/s,and 86.76 m3/s,Root-Mean-Square Error(RMSE)of 84.99 m3/s,84.81 m3/s,and 128.18 m3/s,a determination coefficient ≥0.999 4,Theil Inequality Coefficient ≤0.005 17,showing superior prediction accuracy and generalization ability when compared to the other 12 models.2)DBO,GTO,and MRA effectively optimize the parameters of RELM networks and enhance prediction performance.3)Incorporating a regularization term in RELM prevents overfitting,boosts model generalization abili-ty,and outperforms ELM and BP networks.4)The proposed models exhibit high prediction accuracy,low compu-tational complexity,proving to be efficient for estimating daily inflow runoff time series.
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