Predicting Daily Ozone Concentration Maxima Using Fuzzy Time Series Based on Two-Stage Partition Method
Air pollution is associated with health effects,therefore air pollution is an important and popular topic. In Taiwan, the pollutant standard index (PSI) has been adopted to assess air pollution. The report of primary focus is for health,and the current PSI sub-indices reflect the PM 10 and O 4 measu...
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| creator | Sue-Fen Huang Ching-Hsue Cheng |
| description | Air pollution is associated with health effects,therefore air pollution is an important and popular topic. In Taiwan, the pollutant standard index (PSI) has been adopted to assess air pollution. The report of primary focus is for health,and the current PSI sub-indices reflect the PM 10 and O 4 measured concentrations. Therefore, this study uses O 3 attribute to evaluate air quality. This paper proposes a new fuzzy time series based on two-stage partition method to predict air quality by daily maximum O 3 concentration: (1) fuzzy time series based on uniform discretion method to partition the universe of discourse; (2) fuzzy time series based on cumulative probability distribution approach to partition the universe of discourse. The proposed methods are verified by practical collected dataset. From the results, the two proposed methods both outperform the listing methods in RMSE. The fuzzy time series methods can provide more accuracy predictions for the daily maxima ozone concentration. |
| doi_str_mv | 10.1109/ETTandGRS.2008.388 |
| format | Conference Proceeding |
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In Taiwan, the pollutant standard index (PSI) has been adopted to assess air pollution. The report of primary focus is for health,and the current PSI sub-indices reflect the PM 10 and O 4 measured concentrations. Therefore, this study uses O 3 attribute to evaluate air quality. This paper proposes a new fuzzy time series based on two-stage partition method to predict air quality by daily maximum O 3 concentration: (1) fuzzy time series based on uniform discretion method to partition the universe of discourse; (2) fuzzy time series based on cumulative probability distribution approach to partition the universe of discourse. The proposed methods are verified by practical collected dataset. From the results, the two proposed methods both outperform the listing methods in RMSE. 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In Taiwan, the pollutant standard index (PSI) has been adopted to assess air pollution. The report of primary focus is for health,and the current PSI sub-indices reflect the PM 10 and O 4 measured concentrations. Therefore, this study uses O 3 attribute to evaluate air quality. This paper proposes a new fuzzy time series based on two-stage partition method to predict air quality by daily maximum O 3 concentration: (1) fuzzy time series based on uniform discretion method to partition the universe of discourse; (2) fuzzy time series based on cumulative probability distribution approach to partition the universe of discourse. The proposed methods are verified by practical collected dataset. From the results, the two proposed methods both outperform the listing methods in RMSE. The fuzzy time series methods can provide more accuracy predictions for the daily maxima ozone concentration.</abstract><pub>IEEE</pub><doi>10.1109/ETTandGRS.2008.388</doi><tpages>5</tpages></addata></record> |
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| subjects | Air pollution Atmospheric measurements Current measurement Educational technology Fuzzy Time Series Geoscience and remote sensing Health information management Humans Management training Predictive models Probability distribution Uniform Discretion Method |
| title | Predicting Daily Ozone Concentration Maxima Using Fuzzy Time Series Based on Two-Stage Partition Method |
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