Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan |
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Authors: | Tzu-Yi Pai Ching-Lin Ho Shyh-Wei Chen Huang-Mu Lo Pao-Jui Sung Shu-Wen Lin Wei-Jia Lai Shih-Chi Tseng Shu-Ping Ciou Jui-Ling Kuo Jing-Tang Kao |
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Affiliation: | 1. Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung, 41349, Taiwan 2. Department of Resources Engineering, National Cheng Kung University, Tainan, 701, Taiwan 3. Environmental Protection Bureau, Taoyuan County Government, Taoyuan, 33001, Taiwan 4. Dali City Administration, Taichung County Government, Dali, Taichung, 41261, Taiwan
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Abstract: | 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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