Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

Pai, Tzu-Yi; Ho, Ching-Lin; Chen, Shyh-Wei; Lo, Huang-Mu; Sung, Pao-Jui; Lin, Shuwen; Lai, Wei-Jia; Tseng, Shih-Chi; Ciou, Shu-Ping; Kuo, Jui-Ling; Kao, Jing-Tang

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

2011

In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to predict hourly particulate matter (PM) including PM10 and PM2.5 concentrations in Banciao City of Taiwan. Their prediction 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 14.10%, 25.62, 5.06, and 0.96, respectively, when predicting PM10. When predicting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R value of 15.24%, 11.57, 3.40, and 0.93, respectively, could be achieved. All statistical values revealed that the predicting 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) GM (1, 1) was an efficiently early warning tool for providing PM information to the inhabitants.

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