Forecasting of ozone episode days by cost-sensitive neural network methods
學年 97
學期 2
出版（發表）日期 2009-03-01
作品名稱 Forecasting of ozone episode days by cost-sensitive neural network methods
作品名稱（其他語言）
著者 Tsai, Che-hui; Chang, Li-chiu; 江旭程; Chiang, Hsu-cherng
單位 淡江大學水資源與環境工程學系
出版者 Elsevier
著錄名稱、卷期、頁數 Science of the total environment 407(6), pp.2124-2135
摘要 Forecasting the occurrence of ozone episode days can be regarded as an imbalanced dataset classification problem. Since the standard artificial neural network (ANN) methods cannot make accurate predictions of such a problem, two cost-sensitive ANN methods, cost-penalty and moving threshold, were used in this study. The models classify each day as episode or non-episode according to the standard of daily maximum 8 h O3 concentration. The ozone measurements from six monitoring stations in Taiwan were used for model training and performance evaluation. Two different input datasets, regional and single-site, were generated from raw air quality and meteorological observations. According to the numerical experiments, the predictions based on the regional dataset are much better than those obtained from the single-site dataset. Two cost-sensitive ANN methods were evaluated by receiver operating characteristic (ROC) curves. It was found that the results obtained by the two approaches are similar. If the misclassification costs are known, the cost-sensitive method can minimise the total costs. If the misclassification costs are unknown, the cost-sensitive ANN can obtain a better forecast than the standard ANN method when an appropriate cost ratio is used. For clean areas where episode days are very rare, the forecasts are poor for all methods.
關鍵字 Photochemical air pollution;Statistical model;Imbalanced dataset classification
語言 en
ISSN 0048-9697
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
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