摘要
发展对数据依赖程度低、快捷实用和精准的模拟预报技术,可为资料缺乏地区径流模拟预测提供有效的解决办法。从数据驱动的角度,提出一种融合相空间重构(PSR)和长短期记忆神经网络(LSTM)的径流预测复合模型PSR-LSTM,在国内外不同气候分区的10个流域(站点)进行验证。结果表明:PSR-LSTM能够提取水文变量的多维子空间特征,并较好预测不同时间尺度的径流变化过程;相较于LSTM,PSR-LSTM预测未来1、3、5、7、9时间步长的纳什效率系数在10个流域平均提高1.49%~9.77%,均方根误差平均降低17.01%~19.72%,对训练数据量的依赖程度相比LSTM降低25%~33%。研究成果可为广大资料短缺流域水文过程模拟和预测提供参考。
Developing low data-dependent,efficient,practical and accurate modeling techniques can provide effective solutions for hydrological simulation and prediction in areas with limited data availability.From a data-driven perspective,a composite streamflow prediction model,PSR-LSTM,which integrates Phase Space Reconstruction(PSR)and Long Short-Term Memory(LSTM)networks,was proposed in this study and validated globally over ten river basins(stations)in different climate zones.The results indicate that the PSR-LSTM can effectively extract multi-dimensional sub-space hydrological features and accurately predict streamflow changes at different time scales.Compared to LSTM,the Nash efficiency coefficient of PSR-LSTM in predictions of future 1 to 9 timesteps is increased by an average of 1.49%to 9.77%over the ten river basins;the root mean square error is reduced by an average of 17.01%to 19.72%.The dependency on the amount of training data is reduced by 25%to 33%for PSR-LSTM compared to LSTM.The research findings obtained in this study provide insights into hydrological simulation and prediction in data-scarce river basins.
作者
师鹏飞
赵酉键
徐辉荣
李振亚
杨涛
冯仲恺
SHI Pengfei;ZHAO Youjian;XU Huirong;LI Zhenya;YANG Tao;FENG Zhongkai(The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China;Yangtze Institute for Conservation and Development,Nanjing 210098,China;Provincial Design Institute of Water Conservancy and Electric Power,Guangzhou 510000,China;Key Laboratory of Watershed Geographic Sciences,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China)
出处
《水科学进展》
EI
CAS
CSCD
北大核心
2023年第3期388-397,共10页
Advances in Water Science
基金
国家自然科学基金资助项目(52279009)
中央高校基本科研业务费专项经费资助项目(B220201010)。
作者简介
师鹏飞(1987-),男,副教授,博士,主要从事流域水文物理规律模拟及水文预报方面研究。E-mail:pfshi@hhu.edu.cn。
