Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runoff and time series settings, CNN-LSTMs have primarily been utilized in rainfall-runoff scenarios. In this study, we extend the application of CNN-LSTMs to time series settings, leveraging lagged streamflow data in conjunction with precipitation and temperature data to predict streamflow. Our results show a substantial improvement in predictive performance in 21 out of 32 HUC8 basins in Nebraska, showcasing noteworthy increases in the Kling-Gupta Efficiency (KGE) values. These results highlight the effectiveness of CNN-LSTMs in time series settings, particularly for spatiotemporal hydrological modeling, for more accurate and robust streamflow predictions.

翻译：径流预测领域取得了显著进展，尤其是引入了前沿机器学习模型。其中，长短期记忆网络（LSTM）和卷积神经网络（CNN）被广泛用于该领域。虽然LSTM能同时适用于降雨-径流模型和时间序列场景，但CNN-LSTM主要被用于降雨-径流模型。在本研究中，我们将CNN-LSTM的应用拓展至时间序列场景，利用滞后径流数据结合降水和温度数据来预测径流。结果表明，在内布拉斯加州32个HUC8流域中，有21个流域的预测性能得到显著提升，其Kling-Gupta效率（KGE）值呈现可观增长。这些结果凸显了CNN-LSTM在时间序列场景中的有效性，特别是对于时空水文建模，能够实现更准确且稳健的径流预测。