基于Prophet-LightGBM的PM2.5浓度预测模型
PM2.5 Concentration Prediction Model Based on Prophet-LightGBM
高洁如 1魏霖静 1李玥 1王开翔2
作者信息
- 1. 甘肃农业大学 信息科学技术学院,甘肃 兰州 730070
- 2. 兰州市生态环境信息中心,甘肃 兰州 730031
- 折叠
摘要
近年来,PM2.5污染问题日益突出,对人们的身体健康和环境质量造成了严重影响,建立准确的PM2.5浓度预测模型对于污染防治和空气质量管理具有重要意义.针对PM2.5时间序列的非线性、高噪声、不平稳特征提出一种将Prophet模型和LightGBM模型相结合的组合模型.为了验证模型的有效性,以兰州市PM2.5浓度数据为例,对比分析了Prophet-LightGBM模型和其他4种预测模型及其在不同季节下的预测效果.结果表明,Prophet-LightGBM模型相较于对比模型能够更准确地预测PM2.5 浓度的变化趋势,RMSE值达 6.557,MAE值达 4.543,MAPE值达14.344%,在夏季和秋季的预测准确度和稳定性方面表现出更优异的性能,RMSE值最优时达3.155,MAE值达2.169,MAPE值达9.4%.
Abstract
In recent years,the issue of PM2.5 pollution has become increasingly prominent,causing serious impacts on people's physical health and environmental quality.Therefore,establishing an accurate PM2.5 concentration prediction model is of great significance for pollu-tion prevention and air quality management.A combined model combining Prophet model and LightGBM model is proposed to address the non-linear,high noise,and non-stationary characteristics of PM2.5 time series.In order to verify the effectiveness of the model,the Prophet Light-GBM model and four other prediction models were compared and analyzed with PM2.5 concentration data in Lanzhou City as an example,as well as their prediction effects in different seasons.The results showed that the Prophet LightGBM model was more accurate in predicting the trend of PM2.5 concentration changes compared to the comparative model.The RMSE value reached 6.557,the MAE value reached 4.543,and the MAPE value reached 14.344%.It showed better performance in predicting accuracy and stability in summer and autumn,with the RMSE value reaching 3.155,the MAE value reaching 2.169,and the MAPE value reaching 9.4%when the RMSE value was optimal.
关键词
PM2.5浓度预测/Prophet模型/LightGBM模型/组合模型Key words
PM2.5 concentration prediction/Prophet model/LightGBM model/composite model引用本文复制引用
基金项目
科技部国家外专项目(G2022042005L)
出版年
2024
国家科技期刊平台
NSTL
万方数据