
基于上海市2014—2020年6种空气污染物浓度的监测数据以及同期的天气预报数据,提出一种经模糊C均值聚类算法优化的随机森林O
3
浓度预测模型。利用互相关分析的方法筛选出2个聚类因子,再利用模糊C均值聚类算法将O
3
浓度分为3种类型,最后利用随机森林建立O
3
浓度预测模型,并对比聚类前后的预测效果。结果表明:前1日的O
3
浓度和PM
10
浓度对预测日的O
3
浓度影响最大,且O
3
浓度的月变化明显。经模糊C均值聚类后,O
3_8 h
浓度预测结果的平均绝对误差和均方根误差分别减小了10.5%和8.8%。随机森林提升了O
3
浓度的预测效果,且聚类后模型的决定系数R
2
增加。
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