基于ForecastNet的径流模拟及多步预测

Runoff Simulation and Multi-step Prediction Based on ForecastNet

径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。

The strong nonlinearity presented by the runoff process constrains the prediction performance of the existing hydrological models.The artificial intelligence methods such as deep learning with strong nonlinear fitting ability can break through the current bottleneck to a certain extent.To effectively extract the nonlinear time-varying feature information of runoff sequences and improve the accuracy of runoff simulation and the multi-step-head forecasting performance,the ForecastNet based runoff prediction model with time-varying structure has been established.The Yajiang River Basin in the upper reaches of the Yalong River is taken as a case study,and comparative analyses among the ForecastNet,traditional hydrological model of SWAT(Soil and Water Assessnent Teol),and neural network models of RNN(Recurrent Neural Network),LSTM(Long Short-Term Memory)and RNN-LSTM are carried out.The results show that the ForcastNet model has strong applicability in long term prediction,and can effectively improve the accuracy of runoff simulation and multi-step-ahead forecasting,thus providing technical support for high-precision real-time runoff prediction.