基于VMD-NGO-LSTM的融雪洪水汛期非平稳性极值径流预测模型及应用OA
Non-Stationary Extreme Runoff Prediction Model of Snowmelt Flood in Flood Season Based on VMD-NGO-LSTM and Its Application
金沟河属于典型的融雪补给流域,受自然环境、气候变化和人类活动等因素的影响,汛期极值径流序列表现出非平稳性及复杂性特征,给流域内汛期极值径流精准预测带来新的挑战.为解决该地区汛期极值径流的非平稳性对于预测结果的影响,引入变分模态分解算法(Variational Mode Decomposition,VMD),提出一种基于北方苍鹰优化算法(Northern Goshawk Optimization,NGO)与长短期记忆神经网络(Long Short-…查看全部>>
Jingou River is a typical snowmelt recharge basin.Due to the influence of natural environments,climate changes,and human activities,the extreme runoff sequence in flood season shows non-stationary and complex characteristics,which brings new challenges to the accurate prediction of extreme runoff of the basin in flood season.In order to eliminate the influence of the non-stationarity of extreme runoff in the flood season on the prediction results in the basi…查看全部>>
周霞;周峰
新疆农业大学水利与土木工程学院,新疆 乌鲁木齐 830052||新疆水利工程安全与水灾害防治重点实验室,新疆 乌鲁木齐 830052新疆农业大学水利与土木工程学院,新疆 乌鲁木齐 830052||新疆水利工程安全与水灾害防治重点实验室,新疆 乌鲁木齐 830052
水利科学
融雪洪水极值径流预测变分模态分解北方苍鹰优化算法长短期记忆神经网络非平稳性
snowmelt floodextreme runoff predictionvariational mode decompositionnorthern goshawk optimizationlong short-term memory neural networknon-stationarity
《人民珠江》 2024 (6)
127-137,11
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