用图卷积循环网络+残差纠错，提升1-6小时洪水预报精度。
Short-term Streamflow and Flood Forecasting based on Graph Convolutional Recurrent Neural Network and Residual Error Learning
- 结合图卷积与循环网络捕捉水文时空特征
- 在1-6小时预报窗口优于主流模型，残差学习进一步纠错
- 适合急需高精度短时洪水预警的防灾场景
精准的短期径流与洪水预报对减轻河流洪涝影响至关重要，尤其在气候变率加剧的背景下。基于机器学习的径流预报依赖于由率流曲线生成的大规模径流数据集，而率流曲线建模中的不确定性可能引入数据误差，影响预报准确性。本研究提出一种可应对数据误差的径流预报方法，提升了河流洪水预报与模拟的可靠性，从而降低洪水风险。采用卷积循环神经网络捕捉时空模式，并结合残差误差学习与预报。该神经网络在1-6小时预报时域内表现优于常用模型，残差误差学习器能进一步修正残差误差。该方法为洪水风险缓解的关键1-6小时窗口提供了更可靠的预报工具。
原文摘要 · Abstract (English)
Accurate short-term streamflow and flood forecasting are critical for mitigating river flood impacts, especially given the increasing climate variability. Machine learning-based streamflow forecasting relies on large streamflow datasets derived from rating curves. Uncertainties in rating curve modeling could introduce errors to the streamflow data and affect the forecasting accuracy. This study proposes a streamflow forecasting method that addresses these data errors, enhancing the accuracy of river flood forecasting and flood modeling, thereby reducing flood-related risk. A convolutional recurrent neural network is used to capture spatiotemporal patterns, coupled with residual error learning and forecasting. The neural network outperforms commonly used forecasting models over 1-6 hours of forecasting horizons, and the residual error learners can further correct the residual errors. This provides a more reliable tool for river flood forecasting and climate adaptation in this critical 1-6 hour time window for flood risk mitigation efforts.
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