基于ASWPD-BO-GRU的月径流量预测模型  

Monthly runoff prediction model based on ASWPD-BO-GRU

作　　者：唐铭泽 杨银科[1] 张菁雯 TANG Mingze;YANG Yinke;ZHANG Jingwen(Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region,School ofWater and Environment,Chang’an University,Xi’an 710054,China)

机构地区：[1]长安大学水利与环境学院旱区地下水文与生态效应教育部重点实验室,陕西西安710054

出　　处：《水资源与水工程学报》2023年第4期84-91,共8页Journal of Water Resources and Water Engineering

基　　金：长安大学中央高校基本科研业务费专项资金项目(300102292903);中国科学院黄土与第四纪地质国家重点实验室开放基金项目(SKLLQG1933);陕西省自然科学基础研究计划项目(2017JM4018、2021SF-497)。

摘　　要：为提高月径流量预测精度,并针对传统分解集成径流预测模型错误使用未来数据的问题,提出并建立了基于自适应小波包分解(ASWPD)和贝叶斯优化(BO)的门控循环单元(GRU)月径流量预测模型(ASWPD-BO-GRU)。首先,利用ASWPD对原始月径流量时间序列进行分解,在不使用未来数据的前提下得到4个相对规律的分解子序列,以降低预测难度;然后,利用BO优选分解后的子序列对应的GRU模型超参数;最终,对每个子序列进行预测,将预测结果相加重组得出月径流量预测结果。将提出并建立的模型应用于黑河流域莺落峡水文站月径流量预测中,并与GRU、BO-GRU、WPD-BO-GRU模型(基于传统分解思想对原始月径流量时间序列整体进行分解的预测模型)的预测结果进行对比。结果表明:ASWPD-BO-GRU模型的纳什效率系数(NSE)为0.89,在实例应用中预测精度最高,说明ASWPD-BO-GRU模型在正确分解的前提下具有较高的预测精度和更强的泛化能力。To improve the accuracy of monthly runoff prediction and to address the problem that the conventional decomposition integrated runoff prediction models incorrectly uses future data,a gated recurrent unit(GRU)monthly runoff prediction model(ASWPD-BO-GRU)based on self-adaptation strategy wavelet packet decomposition(ASWPD)and Bayesian optimization(BO)is proposed and developed.First,in order to reduce the prediction difficulty the original monthly runoff time series is decomposed using ASWPD,by which four relatively regular decomposed subseries are obtained without using future data.Then,the hyperparameters of the GRU model corresponding to the decomposed subseries are optimized using BO.Finally,the monthly runoff prediction results are obtained by predicting each subseries and summing and reorganizing the prediction results.The proposed and established model is applied to the prediction of monthly runoff at Yingluoxia Hydrological Station in the Heihe River Basin,and the prediction results are compared with those of GRU,BO-GRU,and WPD-BO-GRU models(models based on the conventional decomposition idea which decomposes the original monthly runoff time series as a whole).The results show that the Nash-Sutcliffe efficiency coefficient(NSE)of ASWPD-BO-GRU model is 0.89,which has the highest prediction accuracy in the example application,indicating that the ASWPD-BO-GRU model has higher prediction accuracy and stronger generalization ability with correct decomposition.

关 键 词：月径流量预测 自适应动态分解策略 小波包分解 贝叶斯优化 门控循环单元 

分 类 号：TV1214[水利工程—水文学及水资源] P338.2[天文地球—水文科学]