融合气象要素时空特征的深度学习水文模型

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。

Development of a spatiotemporal deep-learning-based hydrological model

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.