基于改进BCCSA和深层LSTM的空气质量预测方法  

Air quality prediction method based on improved BCCSA and deep LSTM

作　　者：韦诗玥 徐洪珍[1] Wei Shiyue;Xu Hongzhen(School of Information Engineering,East China University of Technology,Nanchang 330013,China)

机构地区：[1]东华理工大学信息工程学院,江西南昌330013

出　　处：《电子技术应用》2022年第6期28-32,共5页Application of Electronic Technique

基　　金：江西省教育厅科技计划项目(GJJ160554);江西省放射性地学大数据技术工程实验室开放项目(JELRGBDT201802)。

摘　　要：现有的空气质量预测方法很少考虑季节性因素,且预测的效果不佳,因此提出一种基于改进二元混沌乌鸦搜索算法(Binary Chaotic Crow Search Algorithm,BCCSA)和深层长短期记忆神经网络(Long Short Term Memory,LSTM)的空气质量预测方法。首先提出季节调整的方法对收集的原始空气质量数据进行预处理,以消除季节对预测的影响;然后提出改进BCCSA,对空气质量数据进行优化处理;最后,将自注意力机制加入到深层LSTM中,对空气质量数据进行预测。实验结果表明,该方法能有效地提高空气质量的预测精度。The existing air quality prediction methods rarely consider seasonal factors,and the prediction effect is not good.Therefore,an air quality prediction method based on improved binary chaotic crow search algorithm(BCCSA)and deep long short term memory neural network(LSTM)is proposed.Firstly,the method of seasonal adjustment is proposed to preprocess the collected original air quality data in order to eliminate the influence of season on prediction.Then,an improved BCCSA is proposed to optimize the air quality data.Finally,the self-attention mechanism is added to the deep LSTM to predict the air quality data.The experimental results show that this method can effectively improve the prediction accuracy of air quality.

关 键 词：空气质量 季节调整 改进BCCSA 深层LSTM 自注意力机制 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]