基于图卷积循环神经网络和残差学习的短期径流与洪水预报
Short-term Streamflow and Flood Forecasting based on Graph Convolutional Recurrent Neural Network and Residual Error Learning
AI总结：
针对水位流量关系曲线数据误差影响预报精度的问题，本研究提出结合图卷积循环神经网络捕捉时空模式与残差学习修正误差的方法，提升1-6小时短期径流与洪水预报准确性，降低洪水风险。
摘要：
准确的短期径流和洪水预报对于减轻河流洪水影响至关重要，尤其是在气候变化日益加剧的背景下。基于机器学习的径流预报依赖于由水位流量关系曲线生成的大量径流数据集。水位流量关系曲线建模的不确定性可能会给径流数据引入误差，影响预报精度。本研究提出一种径流预报方法，解决这些数据误差问题，提高河流洪水预报和洪水建模的准确性，从而降低洪水相关风险。该方法使用卷积循环神经网络捕捉时空模式，结合残差学习和预报。该神经网络在1-6小时预报时段内优于常用预报模型，残差学习器可进一步修正残差误差。这为洪水风险缓解工作的关键1-6小时窗口期提供了更可靠的河流洪水预报和气候适应工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
