Volume 45 Issue 10
Oct.  2020
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XU Shan, ZOU Bin, WANG Min, LIU Ning. Performance Comparison of Artificial Neural Network and Kriging in Spatial Estimation of PM2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1642-1650. DOI: 10.13203/j.whugis20180482
Citation: XU Shan, ZOU Bin, WANG Min, LIU Ning. Performance Comparison of Artificial Neural Network and Kriging in Spatial Estimation of PM2.5 Concentration[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10): 1642-1650. DOI: 10.13203/j.whugis20180482
shu

Performance Comparison of Artificial Neural Network and Kriging in Spatial Estimation of PM2.5 Concentration

  • 1.

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

  • 2.

    The Third Surveying and Mapping Institute of Hunan Province, Changsha 410004, China

Funds: 

The National Key Research and Development Program of China 2016YFC0206205

the National Natural Science Foundation of China 41871317

the Innovation Driven Program of Central South University 2018CX016

More Information
  • Author Bio:

    XU Shan, PhD candidate, specializes in fine spatial-temporal modeling of urban air pollution. E-mail: shan_xu@csu.edu.cn

  • Corresponding author:

    ZOU Bin, PhD, professor. E-mail: 210010@csu.edu.cn

  • Received Date: December 26, 2019
  • Published Date: October 04, 2020
    Graphical Abstract
    • Abstract
  • Performance of artificial neural network modeling and Kriging interpolation in PM2.5 concentration estimation varies with sample sizes and predictor variables change. This paper analyzes the performance of ordinary Kriging (OK), radical basis function (RBF) networks based on geographic coordinates, CoKriging and RBF with the key factor(s) (CK and CoRBF) selected by correlation analysis and RBF network, using different training sets with various sizes. The spatial distribution of PM2.5 concentration is then estimated by the best performed method. Results show that RBF, CoRBF, OK, and CK can all be used to estimate PM2.5 concentration efficiently, and their accuracies improved unstably as the number of training sites increase. CoRBF with the key factor of population illustrates the largest variation of PM2.5 concentration, while CK has the highest coefficient of determination (R2) and index of agreement (IOA) and the lowest mean square error (MSE), mean absolute error (MAE), and relative error (RE). Correspondingly, the spatial pattern of CK estimated PM2.5 concentration is smoother than CoRBF estimated PM2.5 concentration, while they both are very similar to site measurements and reveal detailed information.
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    • References (23)
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