基于集成学习与深度学习的日供水量预测方法  

Daily water supply prediction method based on integrated learning and deep learning

作　　者：周欣磊 顾海挺 刘晶 许月萍[1] 耿芳 王冲 ZHOU Xin-lei;GU Hai-ting;LIU Jing;XU Yue-ping;GENG Fang;WANG Chong(College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,China;Zhejiang Hydrology New Technology Development Company,Hangzhou 310009,China)

机构地区：[1]浙江大学建筑工程学院,浙江杭州310058 [2]浙江水文新技术开发经营公司,浙江杭州310009

出　　处：《浙江大学学报（工学版）》2023年第6期1120-1127,共8页Journal of Zhejiang University：Engineering Science

基　　金：浙江省自然科学基金资助项目(LZ20E090001);国家重点研发项目(2019YFC0408800)。

摘　　要：为了有效改善日供水量预测精度低、泛化能力不足的问题,以义乌市4个水厂的历史日供水数据为基础,提出基于集成学习算法改进的长短时记忆(LSTM)神经网络的供水预测方法.该方法以拉依达准则预处理后的历史日供水量作为数据输入,将具备长期时序信息记忆能力的LSTM神经网络作为集成学习的弱预测器,使用网格搜索法进行网络超参数调优,使用AdaBoost集成学习算法对弱预测器进行加权组合得到强预测器.结果表明:与随机森林、AdaBoost与LSTM神经网络相比,基于集成学习算法改进的LSTM神经网络有最高的纳什效率系数(NSE)、最低的均方根误差(RMSE)与平均绝对误差(MAE),对日供水数据的变化趋势与峰值的拟合效果最好;改进LSTM供水预测方法的时序预测精度得到极大提升,有较好的泛化能力、稳定的预测性能,能够为城市水资源合理配置、一体化智能供水调度提供重要参考.Making use of the historical daily water supply data of four water plants in Yiwu city,a new water supply prediction model based on long short term memory(LSTM)neural network improved by integrated learning algorithm was proposed,in order to effectively resolve the problems of low accuracy and insufficient generalization ability of the daily water supply prediction.In the model,a historical daily water supply after pre-processing by Pauta criterion was taken as the data input,the LSTM neural network with long-term temporal information memory was applied as the weak predictor of integrated learning,the grid search method was utilized for network hyperparameter tuning,and the AdaBoost integrated learning algorithm was used to weight the combination of the weak predictors to obtain the strong predictor.Results show that the improved LSTM neural network based on integrated learning algorithm has the highest Nash efficiency coefficient(NSE)with the lowest root mean square error(RMSE)and mean absolute error(MAE),the best fitting effect on the change trend and the peak value of daily water supply data,compared with the random forest(RF),AdaBoost and LSTM neural network.The time series prediction accuracy of the improved LSTM water supply forecasting model is significantly improved,with good generalization ability and stable prediction performance.The results can provide an important reference for the rational allocation of urban water resources planning and integrated intelligent water supply scheduling.

关 键 词：供水量预测 集成学习 深度学习 组合模型 长短时记忆(LSTM)神经网络 

分 类 号：TV213[水利工程—水文学及水资源]