摘要
为解决现有基于LSTM的径流预测模型易陷入局部最优的问题,提出了基于VMD-LSTMBLS(variational mode decomposition-LSTM-broad learning system)的径流预测模型。将宽度学习系统与LSTM结合,针对径流序列多噪音特点,采用时频分析方法中的变分模态分解,将径流时间序列的一维时域信号变换到二维时频平面,减少噪声对预测结果的影响。仿真结果表明:与基线模型及现有基于LSTM的径流预测模型相比,该模型的预测精度有较为明显的提高。
Broad learning system(BLS)is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization.To reduce the influence of noise on the prediction results,the variational mode decomposition(VMD)is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane.The runoff prediction model based on VMD-LSTM-BLS is proposed.The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.
作者
韩莹
王乐豪
王淑梅
张翔
罗星星
Han Ying;Wang Lehao;Wang Shumei;Zhang Xiang;Luo Xingxing(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science&Technology,Nanjing 210044,China;Xinjiang Raohe Hydrological and Water Resources Testing Center,Shangrao 334000,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2024年第2期363-372,共10页
Journal of System Simulation
基金
国家自然科学基金(62076136)
教育部新农科研究与改革实践(20200251)。
关键词
径流预测
变分模态分解
长短时记忆网络
宽度学习系统
时频分析
智能预测
runoff forecast
variational mode decomposition
long and short-term memory network
broad learning system
time-frequency analysis
intelligent prediction