基于相关性检验的VMD-LSTM耦合模型月径流模拟研究  

Simulation of monthly runoff by VMD-LSTM coupled model based on correlation testing

作　　者：刘声洪 SOOMRO Shan-E-Hyder 李颖 李英海[1,2] 程雄[1,2] 杨少康[3] LIU Shenghong;SOOMRO Shan-E-Hyder;LI Ying;LI Yinghai;CHENG Xiong;YANG Shaokang(College of Hydraulic&Environmental Engineering,China Three Gorges University,Yichang 443002,China;Hubei Key Laboratory of Hydropower Engineering Construction and Management,Yichang 443002,China;Xiantao River Embankment Administration,Xiantao 433000,China)

机构地区：[1]三峡大学水利与环境学院,湖北宜昌443002 [2]三峡大学水电工程施工与管理湖北省重点实验室,湖北宜昌443002 [3]仙桃市河道堤防管理局,湖北仙桃433000

出　　处：《水资源与水工程学报》2024年第2期71-82,共12页Journal of Water Resources and Water Engineering

基　　金：国家自然科学基金项目(52179018,51909010);国家重点研发计划课题(2022YFC3203902-3);智慧长江与水电科学湖北省重点实验室(中国长江电力股份有限公司)开放基金项目(ZH2002000103);长江科学院开放研究基金项目(CKWV2021889/KY)。

摘　　要：近年来,极端强降雨和干旱事件频发,流域水文过程的不确定性变化加剧,使得流域中长期径流预测的难度增加。为提升LSTM(长短期记忆神经网络)模型对径流时序变化的捕捉及拟合能力,以博阳河流域为研究区域,选取月降雨、蒸发及流量数据,利用VMD(变分模态分解)和相关性检验,排除无关频率分量对LSTM模型规律学习的干扰,以达到模型输入优选的目的;此外,还考虑了VMD与LSTM模型的不同耦合方式对模型精度和稳定性的影响,最终优选出二者兼具的VMD-LSTM月径流耦合模式。结果表明:VMD-LSTM耦合模型可显著提升模拟精度,但在模型稳定性方面有所欠缺;而基于相关性检验的VMD-LSTM耦合模型不仅能够进一步提高模型精度,并且在模型的稳定性方面也有所改进。在基于相关性检验的VMD-LSTM耦合模型的不同耦合方式对比中,对输入、输出均进行VMD分解且对输入变量进行优选的D_(1)耦合方案的模拟效果最好,其60次模拟计算的NSE均为0.98以上且稳定性极佳;另外,在分析方案D_(1)的可解释性时发现历史径流对于LSTM模型的影响要比降雨和蒸发大。该研究结论可为流域水资源管理提供精准可信的中长期径流模拟成果。In recent years,the frequent occurrence of extreme heavy rainfall and drought events has intensified the uncertainty changes of hydrological processes in the basin,making it more difficult for the prediction of medium and long-term runoff in the basin.In order to improve the ability of LSTM(long short-term memory)model in capturing and fitting temporal changes in runoff,taking the Boyang River Basin as the study area,we collected the data of monthly rainfall,evaporation and flow for the simulation.Then,VMD(variational modal decomposition)and correlation testing are used to eliminate the interference of irrelevant frequency components on the regular learning of LSTM model for the purpose of model input optimization.In addition,the effects of different coupling methods between VMD and LSTM model on the accuracy and stability of the model are considered,by which the VMD-LSTM monthly runoff coupled model which takes the advantages of both is finally selected.The simulation results show that the VMD-LSTM coupled model can significantly improve the simulation accuracy but it is lacking in model stability,the VMD-LSTM coupled model based on correlation testing can not only further improve the model accuracy,but also improves the stability of the model.In the comparison of the different coupling methods of the VMD-LSTM coupled model based on the correlation testing,scheme D_(1)which decomposes both inputs and outputs with VMD and optimizes inputs has the best simulation accuracy and stability,with an NSE value of 0.98 and above for 60 simulations.In addition,when analyzing the interpretability of scheme D_(1),it is found that historical runoff has a greater impact on the LSTM model than rainfall and evaporation.The results of this study can provide accurate and reliable medium and long-term runoff simulations for the water resources management in the basin.

关 键 词：相关性检验 变分模态分解 长短期记忆神经网络 径流模拟 博阳河流域 

分 类 号：P338[天文地球—水文科学]