基于注意力机制的CNN-ILSTM地铁站PM2.5预测建模
Prediction modeling of PM2.5 in subway station based on attention mechanism and CNN-ILSTM
朱菊香 1谷卫 2罗丹悦 2潘斐 2张赵良1
作者信息
- 1. 无锡学院轨道交通,江苏无锡 214105
- 2. 南京信息工程大学自动化学院,江苏南京 210000
- 折叠
摘要
为提高PM2.5的预测精度,提出一种基于卷积神经网络(CNN)、改进长短期记忆网络(ILSTM)和注意力机制(attention)组合的预测模型.ILSTM删除LSTM中的输出门,改进其输入门和遗忘门,并引入转换信息模块(CIM),以防止学习过程中的过饱和.该模型将一维卷积神经网络的特征提取和改进长短期记忆网络学习序列依赖性的能力相结合,得到过去不同时间特征状态对未来PM2.5 浓度的影响,可以有效模拟PM2.5 在时间和空间上的依赖性,并通过注意力机制自动权衡过去的特征状态,进一步提升PM2.5 预测的准确度.实验结果表明:CNN-ILSTM-attention模型的拟合度达到 98.5%,与LSTM模型、CNN-LSTM模型和CNN-ILSTM模型相比,分别提高 26%、9.2%和 6.2%,具有较高的预测精度和应用价值.
Abstract
In order to improve the prediction accuracy of PM2.5,a combined prediction model based on convolutional neural network(CNN),improved long short-term memory network(ILSTM)and attention mechanism was proposed.ILSTM removes the output gate in LSTM,improves its input gate and forget gate,and introduces a transition information module(CIM)to prevent oversaturation during learning.One-dimensional convolutional neural network models of feature extraction and the ability to improve both short-term and long-term memory network learning sequence dependent state of different time of the past,the combination of characteristics of PM2.5 concentrations in the future,can effectively simulate the PM2.5 dependent on time and space,and through the attention mechanism automatic weighing the characteristics of the state in the past,to further improve the accuracy of PM2.5 prediction.The experimental results show that the fitting degree of CNN-ILSTM-attention model reaches 98.5%,which is improved by 26%,9.2%and 6.2%,respectively,compared with LSTM model,CNN-LSTM model and CNN-ILSTM model.It has high prediction accuracy and application value.
关键词
卷积神经网络/改进长短期记忆网络/PM2.5浓度预测/注意力机制Key words
convolutional neural network/improved long short-term memory network/PM2.5 concentration prediction/attention mechanism引用本文复制引用
基金项目
国家自然科学青年基金(51206082)
江苏省自然科学基金(be2015692)
江苏省高等学校自然科学研究项目(21KJB460005)
出版年
2024
NETL
NSTL
万方数据