融合气象要素时空特征的深度学习水文模型 |
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引用本文: | 李步,田富强,李钰坤,倪广恒.融合气象要素时空特征的深度学习水文模型[J].水科学进展,2022,33(6):904-913. |
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作者姓名: | 李步 田富强 李钰坤 倪广恒 |
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作者单位: | 清华大学水沙科学与水利水电工程国家重点实验室, 北京 100084 |
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基金项目: | 国家重点研发计划资助项目2018YFA0606002 |
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摘 要: | 针对现有深度学习水文模型未能充分刻画气象要素空间特征的问题, 本文基于主成分分析(PCA)方法提取气象要素空间特征, 利用长短时记忆神经网络(LSTM)学习长时序过程规律, 构建融合气象要素时空特性的深度学习水文模型PCA-LSTM。以黄河源区为研究区域, 利用LSTM模型和物理水文模型THREW作为比对模型, 基于高斯噪音法系统评估PCA-LSTM模型的适用性和鲁棒性。结果显示: PCA-LSTM模型径流模拟纳什效率系数为0.92, 高于比对模型LSTM和THREW, 表明模型具有较高的精度。研究结果可为流域高精度水文模拟提供参考。
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关 键 词: | 水文模拟 物理水文模型 深度学习 长短时记忆神经网络 主成分分析 黄河源区 |
收稿时间: | 2022-08-18 |
Development of a spatiotemporal deep-learning-based hydrological model |
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Affiliation: | State Key Laboratory of Hydro science and Engineering, Tsinghua University, Beijing 100084, China |
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Abstract: | Deep learning has been proven to show remarkable performance in hydrological modeling; however, the spatial features of meteorological data are rarely incorporated in current deep learning hydrological models. In this study, we propose a spatiotemporal DL-based hydrological model by coupling principal component analysis (PCA) and long short-term memory (LSTM). PCA and LSTM were used to capture the spatial characteristics of meteorological data and understand long-length temporal dynamics, respectively. We used the source region of the Yellow River to test the PCA-LSTM model and compared the results with those of LSTM-only and THREW models. The Gaussian noise method was also used to evaluate the robustness of the PCA-LSTM model. The proposed PCA-LSTM model showed better performance than THREW and LSTM models, with Nash-Sutcliffe efficiency coefficients of 0.92, underlining the potential of the PCA-LSTM model for hydrological modeling and prediction. |
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