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- W4293796565 abstract "Using surface solar radiation (SSR) during 2015–2019 and the corresponding meteorological variables, pollution observations and temporal code, random forest models were built for the seven stations in North China Plain. The permutation feature importance (PFI) and partial dependence plot (PDP) reveal that, besides temporal code, total cloud cover (TCC) and aerosol optical depth (AOD) are the most important influencing feature on global radiation (GR) and diffuse radiation (DR), respectively. Single feature PDP visualized how the input variables affect the estimated SSR. The different order in PFI of boundary layer height (BLH) between urban and farmland stations and the distinct variations in PDP of BLH among urban stations suggest the interaction of BLH and aerosols has substantial impact on the SSR in metropolis areas. The joint PDP displayed the nonlinear relationships between SSR and the input feature pairs. The change range of SSR with the corresponding input features and feature pairs is also investigated, with the maximum variation of GR from 63.4 Wm−2 to 231.9 Wm−2 under the influence of TCC − BLH and of DR from 51.8 Wm−2 to 104.9 Wm−2 under the impact of TCC − AOD in Zhengzhou, respectively. These findings provide insight into the simulation and prediction of SSR, temperature and subsequent weather conditions in urban areas." @default.
- W4293796565 created "2022-08-31" @default.
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- W4293796565 date "2022-12-01" @default.
- W4293796565 modified "2023-09-24" @default.
- W4293796565 title "Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning" @default.
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- W4293796565 doi "https://doi.org/10.1016/j.atmosres.2022.106406" @default.
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