Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong. (October 2016)

• Record Type: Journal Article
• Authors: Gong, Bing; Ordieres-Meré, Joaquín
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 290
• Page End: 303
• Publication Date: 2016-10
• Subjects: Ozone level forecasting -- Classification -- Artificial intelligence -- Re-sampling -- Imbalanced data -- Ensemble models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.06.020
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem. Ensemble algorithms are proposed to improve the classifier's accuracy. Moreover, a distance-based regional data set was generated to capture ozone transportation characteristics. The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances. Furthermore, this study advises on the relative importance of the different variables for ozone pollution prediction and confirms that regional data facilitate better forecasting. The results of this research can be promoted by the Hong Kong authorities for improving the existing forecasting tools. Moreover, the results can facilitate researchers' selection of the appropriate techniques in their future research. Graphical abstract: Highlights: Prediction of daily ozone exceedances in Hong Kong using Artificial Intelligence. Intelligent preprocessing methods and Ensemble models were applied for prediction. Forecast was based on the local and regional pollution and meteorological data. Regional data helped to perform better forecasting than local data did. Identification of ozone generation and ozone transportation behaviors.