Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data

被引:1
|
作者
Wang, Fuzeng [1 ]
Liu, Ruolan [2 ,3 ,4 ]
Yan, Hao [5 ]
Liu, Duanyang [4 ]
Han, Lin [3 ]
Yuan, Shujie [3 ]
机构
[1] Chengdu Univ Informat Technol, Coll Elect Engn, Chengdu 610225, Peoples R China
[2] Chengdu Meteorol Off, Pengzhou Meteorol Adm, Chengdu 611930, Peoples R China
[3] Chengdu Univ Informat Technol, Sch Atmospher Sci, Chengdu 610225, Peoples R China
[4] Nanjing Joint Inst Atmospher Sci, Key Lab Transportat Meteorol China Meteorol Adm, Nanjing 210041, Peoples R China
[5] Shanghai Maritime Univ, Coll Ocean Sci & Engn, Shanghai 200135, Peoples R China
关键词
Decision tree algorithm; Random forest algorithm; Visibility prediction; Haze; Artificial intelligence; FOG; EVENTS; HAZE; MICROPHYSICS; AEROSOL; IMPACT; CHINA; MODEL; PM1;
D O I
10.1016/j.apr.2024.102270
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.
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页数:10
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