| 丁红,王伟泽,杨泽凡,刘欢,胡鹏.基于深度学习和信号分解的北方寒区河流开河日期预报[J].水利学报,2024,55(5):577-585 |
| 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 |
| 投稿时间：2023-09-08 |
| DOI：10.13243/j.cnki.slxb.20230549 |
| 中文关键词: 河流开河日期 信号分解技术 深度学习 预报方法 北方寒区 |
| 英文关键词: break-up date of river ice signal decomposition technology deep learning forecasting method northern China |
| 基金项目:国家重点研发计划课题(2022YFF1300902);国家自然科学基金项目(52122902，42001040);流域水循环模拟与调控国家重点实验室自主研究课题(SKL2022ZD01);中国水利水电科学研究院基本科研业务费项目(WR0145B022021) |
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| 中文摘要: |
| 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，CEEMDAN-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。 |
| 英文摘要: |
| 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 reference 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，compared 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. |
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