Comparisons of GM (1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan

Developmental Biology

Li Chen¹  Tzu-Yi Pai² 

[1] General Education Center, Shuzen Junior College of Medicine and Management, Luju, Kaohsiung City, 82144, Taiwan, ROC$$;Master Program of Environmental Education and Management, Department of Science Education and Application, National Taichung University of Education, Taichung City, 40306, Taiwan, ROC$$

关键词: Grey system theory;    GM (1;    1);    hourly particulate matter;    back–propagation neural network;   

DOI  :  10.5094/APR.2015.064

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

This paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM_2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM₁₀ prediction. For PM_2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, 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) could predict the hourly PM variation precisely even comparing with BPNN.

【 授权许可】

Unknown   

【 预 览 】

附件列表

Files	Size	Format	View
RO201912040527904ZK.pdf	747KB	PDF