摘要
基于长短时记忆网络(Long Short-Term Memory, LSTM)降水量预测模型存在过拟合、时滞现象,而宽度学习系统(Broad Learning System, BLS)无需多次迭代的特点有助于解决LSTM的上述缺点.加权宽度学习系统(Weighted Broad Learning System, WBLS)通过在BLS中引入加权惩罚因子约束分配样本权重,降低噪声和异常值对降水量预测精度的影响.本文提出一种LSTM-WBLS日降水量预测模型,选取湖北省巴东站日降水量进行实证研究,并考虑气压、气温、湿度、风速和日照等因素对降水量的影响.实验结果表明,与现有的预测模型相比,LSTM-BLS模型在RMSE、MAE和R^(2)等评价指标上均有显著提升.不同时间步长下,本文模型预测精度均优于现有模型,验证了其稳定性.与LSTM相比,WBLS直接计算权重的特点使得LSTM-WBLS的运算效率并未降低.
The popular Long Short-Term Memory(LSTM)based precipitation prediction models suffer from overfitting and time lag.Broad Learning System(BLS),which does not require multiple iterations,helps to solve the above disadvantages of LSTM.Weighted Broad Learning System(WBLS)reduces the impact of noise and outliers on precipitation prediction accuracy by introducing a weighted penalty factor constraint to assign sample weights in the BLS.Thus a LSTM-WBLS daily precipitation prediction model is proposed in this paper.The daily precipitation at Badong station in Hubei province is selected for empirical study.And the influence of air pressure,temperature,humidity,wind speed and sunshine on precipitation is considered.The experimental results demonstrate that the LSTM-BLS model has significantly improved the prediction accuracy in the evaluation indexes of RMSE,MAE and R^(2) compared with existing prediction models.The prediction accuracy of the new model outperforms existing models at different time steps,proving its stability.In particular,the direct calculation of weights by WBLS does not make any reduction in operational efficiency of LSTM-WBLS.
作者
韩莹
管健
曹允重
罗嘉
HAN Ying;GUAN Jian;CAO Yunzhong;LUO Jia(School of Automation,Nanjing University of Information Science&Technology,Nanjing 210044;Hubei Public Meteorological Service Center,Wuhan 430074)
出处
《南京信息工程大学学报（自然科学版）》
CAS
北大核心
2023年第2期180-186,共7页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
南方海洋科学与工程广东省实验室(珠海)基金(SML2020SP007)
国家自然科学基金(62076136)。
关键词
降水量预测
长短时记忆网络
宽度学习系统
加权宽度学习系统
多因素预测
precipitation forecast
long short-term memory(LSTM)network
broad learning system(BLS)
weighted broad learning system(WBLS)
multi-factor predication
