气象与环境学报2025，Vol.41Issue(2)：56-63,8.DOI:10.3969/j.issn.1673-503X.2025.02.007
基于多种机器学习算法构建的呼和浩特市臭氧气象条件评估指数对比分析
Comparative analysis of ozone meteorological condition assessment indices for Hohhot city based on multiple machine learning algorithms
摘要
Abstract
Using ozone monitoring data and meteorological data in Hohhot city from 2018 to 2022,this study analy-zes the O3 concentration variation characteristics.Models with relating meteorological factors to predict daily maxi-mum 8-h average O3 concentration(ρ(O3-8h))were established using Light Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting(XGBoost),Random Forest(RF),and Long Short-Term Memory networks(LSTM).A comparison of model performance metrics was conducted to identify the optimal model,which was validated.The results showed that over the past five years,O3 concentration in Hohhot City(ρ(O3-8h)exceeded 160 μg·m-3 from April to October,with the most exceedance days in June and July,followed by May and Au-gust.Among the model input factors,daily maximum temperature contributes the most to the prediction of(ρ(O3-8h),accounting for 44%.The LightGBM model provided the best simulation results,with overall model perform-ance ranking from highest to lowest as follows:LightGBM>LSTM>XGBoost>RF.The locally constructed o-zone meteorological condition assessment index showed a correlation coefficient up to 0.86 with(ρ(O3-8h),an improvement of 17.81%over the China Meteorological Administration's ozone meteorological condition assess-ment index.This demonstrates its effectiveness in assessing meteorological condition influences on O3 concentration variations in Hohhot City.关键词
极端梯度提升树(XGBoost)/随机森林(RF)/长短期记忆网络(LSTM)/轻量级梯度提升机(LightGBM)Key words
Extreme Gradient Boosting(XGBoost)/Random Forest(RF)/Long Short-Term Memory networks(LSTM)/Light Gradient Boosting Machine(LightGBM)分类
环境科学引用本文复制引用
王俊秀,张智,李二杰,姜学恭,杨泽华,王俊杰,兰劲青..基于多种机器学习算法构建的呼和浩特市臭氧气象条件评估指数对比分析[J].气象与环境学报,2025,41(2):56-63,8.基金项目
国家自然科学基金项目(41965003)、内蒙古自治区气象局引导性创新基金项目(nmqxydcx202201,nmqxydcx202202)和内蒙古自治区气象局科技创新项目(nmqxkjcx202301)共同资助. （41965003）
