基于深度学习的西南地区寿溪河山洪预报研究

鉴于山洪突发性强、历时短、陡涨陡落等致使在模拟预报过程中具有较大难度和不确定性问题,构建了基于深度学习的LSTM网络模型进行山洪确定性预报和概率预报,从精度和可靠度两方面研究其在西南山区的适用性.并以西南山洪易发区寿溪河流域为例进行模拟,结果显示LSTM网络模型更易发现暴雨洪水之间的深层规律,验证期平均纳什效率系数达0...

Flash Flood Forecasting of Shouxi River in Southwestern Region Based on Deep Learning

Because flash flood has the characteristics of strong suddenness, short duration, and steep rise and fall, it has greater difficulty and uncertainty in the simulation and forecasting process. In this paper, a deep learning-based LSTM network model is constructed for deterministic and probabilistic forecasting of flash flood, and its applicability in southwest mountainous areas is studied in terms of accuracy and reliability. Taking the Shouxi River Basin in the southwest mountain torrent-prone area as the research area, the results show that the LSTM network model can better find the deep law between storms and floods. The average Nash efficiency coefficient during the verification period reached 0.954, which is a significant improvement compared with the BP model. The accuracy of flood forecasting is improved, especially for large floods. The probability forecast effectively reduces the uncertainty of flash flood forecasting, and the flow data near the flood peak basically falls within the forecast interval, which effectively improves the forecast reliability.