Prediction of AOD data by geographical and temporal weighted regression with nonlinear principal component analysis
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ORIGINAL PAPER
Prediction of AOD data by geographical and temporal weighted regression with nonlinear principal component analysis Guangchao Li 1 & Wei Chen 1
&
Ruren Li 2 & Yijin Chen 1 & Hongru Bi 1 & Haimeng Zhao 3 & Lihe Li 4
Received: 14 May 2020 / Accepted: 20 August 2020 # Saudi Society for Geosciences 2020
Abstract When predicting aerosol optical depth (AOD) values for geographical weighted regression (GWR) and geographical and temporal weighted regression (GTWR), the input variables are influenced by multiple collinearity. Additionally, too many input variables make the model computationally complex, and too few input variables can reduce the prediction accuracy. In this study, a nonlinear principal component analysis geographical weighted regression method (NLPCA-GWR) and a nonlinear principal component analysis geographical and temporal weighted regression method (NLPCA-GTWR) are proposed. The NLPCA-GWR and NLPCA-GTWR methods use nonlinear principal component analysis (NLPCA) to reduce the dimensionality of several related variables that influence the AOD and to obtain several comprehensive indicators. The obtained comprehensive indicators are used as dependent variables that are input into the GWR and GTWR models to predict AOD values. To test the effectiveness of the NLPCA-GWR and NLPCA-GTWR methods, this paper uses Beijing, Tianjin, and Hebei AOD data from April 2015; air quality data; meteorological data; and geospatial data as experimental data to model and compare the GWR and GTWR methods with the same number of input variables. The results show that the MAE, RMSE, AIC, R2, and R2j of the NLPCA-GWR method are 13.58%, 6.99%, 13.86%, 4.07%, and 3.96% higher, respectively, than those of the GWR method. Compared with the GTWR method, the NLPCA-GTWR method improved the MAE, RMSE, AIC, R2, and R2j by 6.53%, 2.91%, 2.17%, 1.14%, and 1.14%, respectively. Keywords Aerosol optical depth . Geographical weighted regression . Geographical and temporal weighted regression . Nonlinear principal component analysis . Beijing-Tianjin-Hebei
Introduction Atmospheric aerosol refers to a multiphase system formed by various liquid and solid particles suspended in the atmosphere Responsible Editor: Amjad Kallel * Wei Chen [email protected] 1
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
2
School of Transportation Engineering, Shenyang Jianzhu University, Shenyang 110168, China
3
Guangxi Engineering Research Center for Small UAV System and Application, Guilin University of Aerospace Technology, Guilin 541004, China
4
Guangxi Zhuang Autonomous Region Eco-environmental Monitoring Center, Nanning 530028, China
with a diameter of 0.001–100 μm and a certain stability. Aerosol mainly scatters, reflects, absorbs, etc. the radiation of the sun and the ground and affects the transmission of solar radiation in the atmosphere (Wei and Sun 2017). The distribution of atmospheric aerosols is closely related to climate change, air quality, environmental poll
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