水力发电2024，Vol.50Issue(1)：1-7,7.
基于EEMD-NGO-LSTM神经网络耦合的月径流预测模型及应用
Monthly Runoff Prediction Model Based on a Coupled EEMD-NGO-LSTM Neural Network and Its Application
摘要
Abstract
In order to improve the stability and accuracy of runoff series and reduce the nonlinear error caused by improper parameter optimization the long-and short-term memory neural network (LSTM) integrated empirical modal decomposition (EEMD) and Northern Goshawk Optimization Algorithm (NGO) are combined to construct a coupled EEMD-NGO-LSTM prediction model.This prediction model is applied to simulate the month-by-month runoff process from 2012 to 2021 at Wangbun Hydrological Station the control terminus in the middle and lower reaches of Dongliao River and the simulation results are compared with that of long-and short-term memory neural networks optimized by the Whale Optimization Algorithm (WOA) as well as the Gray Wolf Algorithm (GWO) respectively.The results show that the coupled EEMD-NGO-LSTM prediction model has the fastest hyperparameter iteration speed the highest accuracy and the closest prediction to the measured value with a coefficient of determination R2 of 0.864 3.The precipitation and temperature data for 2030 under the CMIP6 climate model (SSP126 scenario) are used to input into the model for the prediction and the results show that (a) under the scenario in which the temperature is increased by 1℃ and the precipitation remains unchanged the annual runoff will increase by 6.61％;(b) under the scenario with a 5％increase in precipitation and no change in temperature annual runoff will increase by 6.95％;and (c) under the scenario with a 1℃ increase in temperature and a 5％increase in precipitation annual runoff will increase by 22.16％.关键词
月径流预测/集成经验模态分解/北方苍鹰优化算法/长短期记忆神经网络/耦合模型/预测精度Key words
monthly runoff prediction/integrated empirical modal decomposition ( EEMD)/Northern Goshawk Optimization (NGO)/long- and short-term memory neural network/coupled model/prediction accuracy分类
建筑与水利引用本文复制引用
张冲,王千凤,齐新虎,王思宇,陈末..基于EEMD-NGO-LSTM神经网络耦合的月径流预测模型及应用[J].水力发电,2024,50(1):1-7,7.基金项目
黑龙江省普通本科高等学校青年创新人才培养计划(UNPYSCT-2020012) （UNPYSCT-2020012）
