现代信息科技2024，Vol.8Issue(4)：142-146,152,6.DOI:10.19850/j.cnki.2096-4706.2024.04.030
基于SSA-LSTM模型的空气质量预测研究
Research on Air Quality Prediction Based on SSA-LSTM Model
曹还君 1李长云1
作者信息
- 1. 湖南工业大学 计算机学院,湖南 株洲 412007
- 折叠
摘要
Abstract
To improve the accuracy of PM2.5 concentration prediction,a combined prediction model integrating Sparrow Search Algorithm(SSA)and Long Short-Term Memory(LSTM)neural networks is proposed.The SSA-LSTM model is developed based on PM2.5 concentration data from Changsha city,spanning from May to August in 2023,and is compared with other models.The results show that the SSA-LSTM model significantly outperformed the standalone LSTM,PSO-LSTM,and WOA-LSTM models in terms of fit quality(R2),registering improvements of 45.93％,31.55％,and 19.12％,respectively.Similarly,it also shows superior performance in terms of Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).These findings demonstrate the model has high accuracy and effectiveness in PM2.5 concentration prediction,providing a certain reference value for making the PM2.5-related preventive measures.关键词
麻雀搜索算法/长短期记忆神经网络/空气质量/PM2.5浓度预测Key words
SSA/LSTM/air quality/PM2.5 concentration prediction分类
信息技术与安全科学引用本文复制引用
曹还君,李长云..基于SSA-LSTM模型的空气质量预测研究[J].现代信息科技,2024,8(4):142-146,152,6.
