首页|基于深度学习和信号分解的北方寒区河流开河日期预报

基于深度学习和信号分解的北方寒区河流开河日期预报

Forecasting break-up date of river ice in northern China based on deep learning and signal decomposition technology

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中国北方寒区河流春季开河时易产生冰凌现象,威胁涉河水工建筑物的安全.准确地预测寒区河流开河日期可为防凌指挥、调度决策提供重要参考依据.本文基于中国北方典型寒区-黑龙江省的5个代表水文站近60年的历史开河日期序列,采用完全自适应集合经验模态分解(CEEMDAN)技术和深度学习长短期记忆模型(LSTM)方法构建河流开河日期预报的耦合模型,以期提高河流开河日期预报的精度.结果表明:本研究构建的开河日期预报耦合模型(CEEMDAN-LSTM)预测精度明显优于单一深度学习方法(LSTM)计算结果;与LSTM相比,CEEM-DAN-LSTM 可将开河日期预报的平均绝对误差从2.51 d降低至1.20 d,合格率从91.59％提高至100％.验证期平均绝对误差从3.85 d降低至1.65 d,合格率从88％提高至96％.因此,所构建的开河日期预报耦合模型具有较高的预报精度,可为我国北方寒区春季防凌指挥和调度提供技术支持.
Ice floods occasionally occur during river ice breaking up in northern China in spring,threatening the safety of hydraulic structures.Forecasting the break-up date of river ice(BUDRI)accurately is an important refer-ence for anti-flooding command and dispatching decision-making during ice breaking period.For forecasting the BUDRI in northern China,the observed break-up date series of river ice of 5 representative hydrological stations in Heilongjiang province located in northern China was selected,and the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise technology and deep learning model Long Short Term Memory(CEEMDAN-LSTM)was used to forecast the BUDRI.The results show that the forecast accuracy of CEEMDAN-LSTM,com-pared with LSTM,had been significantly improved with the mean absolute error reduced from 2.51 d to 1.20 d,the qualification rate increased from 91.59％to 100％in the training period.and the mean absolute error reduced from 3.85 d to 1.65 d,the qualification rate increased from 88％to 96％in the validation period.The CEEMDAN-LSTM performed well in forecasting the BUDRI in northern China,which can provide important information for command,dispatch,and decision-making of ice flood control.

break-up date of river icesignal decomposition technologydeep learningforecasting methodnorthern China

丁红、王伟泽、杨泽凡、刘欢、胡鹏

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中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室,北京 100038

西安理工大学土木建筑工程学院,陕西西安 710048

河流开河日期 信号分解技术 深度学习 预报方法 北方寒区

国家重点研发计划国家自然科学基金国家自然科学基金流域水循环模拟与调控国家重点实验室自主研究项目中国水利水电科学研究院基本科研业务费项目

2022YFF13009025212290242001040SKL2022ZD01WR0145B022021

2024

10.13243/j.cnki.slxb.20230549

水利学报
中国水利学会

水利学报

CSTPCD北大核心
影响因子：1.778
ISSN：0559-9350
年,卷(期)：2024.55(5)
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