TVGM-LSTM耦合模型及其径流模拟效果分析

TVGM-LSTM coupling model and its runoff simulation effect analysis

为提升变化环境下流域径流模拟与预测精度,以白河流域为例,构建了基于时变增益水文模型(TVGM)和长短期记忆网络(LSTM)的TVGM-LSTM耦合模型,并利用随机森林算法识别模型最优解释变量。将耦合模型应用于2011—2018年白河流域径流模拟,结果表明:TVGM-LSTM耦合模型在白河流域具有较好的径流模拟效果,率定期与检验期纳什效率系数分别为0.95与0.90;与TVGM相比,耦合模型提升了对非汛期径流的模拟精度,且能够较好地模拟汛期与非汛期洪峰;耦合模型能够有效避免过拟合问题,泛化性能较优,预测精度稳定性较强。

To improve the accuracy of watershed runoff simulation and prediction under changing environment,a TVGM(time variant gain model)-LSTM(long short-term memory)coupling model was constructed for the Baihe River Basin,and the random forest algorithm was used to identify the optimal explanatory variables of the model.The model was applied to simulate the runoff of the Baihe River Basin from 2011 to 2018,and the results show that the TVGM-LSTM coupling model has a good runoff simulation effect in the Baihe River Basin,with the Nash efficiency coefficient reaching 0.95 and 0.90 in the calibration and validation periods,respectively.Compared with TVGM,the TVGM-LSTM coupling model improves the simulation accuracy of non-flood season runoff,and it performs well in simulating flood peaks in flood and non-flood periods.In addition,the TVGM-LSTM coupling model can effectively avoid the overfitting problem,and it has better generalization performance and stable runoff prediction accuracy.