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Forecasting Hourly Roadside Particulate Matter in Taipei County of Taiwan Based on First‐Order and One‐Variable Grey Model
Author(s) -
Pai TzuYi,
Hanaki Keisuke,
Chiou RenJie
Publication year - 2013
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201000402
Subject(s) - mean squared error , statistics , mathematics , mean absolute percentage error , particulates , coefficient of determination , ecology , biology
In this study, seven types of first‐order and one‐variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM 10 and PM 2.5 concentrations in Taipei County of Taiwan. Their forecasting performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient ( R ) was 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM 10 . When forecasting PM 2.5 , the minimum MAPE, MSE, RMSE, and maximum R ‐value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting performance of GM (1, 1, x (0) ), GM (1, 1, a ), and GM (1, 1, b ) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing PM information to the roadside inhabitants.