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.

在这项研究中，我们将 CNN-LSTMs 的应用扩展到时间序列设置中，利用滞后的水流数据与降水和温度数据相结合，预测水流状况，并在内布拉斯加州的 32 个 HUC8 流域中的 21 个流域中取得了显著的预测性能改进，展示了 Kling-Gupta 效率值的显著增加，从而突出了 CNN-LSTMs 在时间序列设置中的效果，特别适用于准确且鲁棒的水流预测的时空水文建模。