Air pollution prediction via multi-label classification. (June 2016)

• Record Type: Journal Article
• Authors: Corani, Giorgio; Scanagatta, Mauro
• Is Part Of: Environmental modelling & software. Volume 80(2016:Jun.)
• Page Start: 259
• Page End: 264
• Publication Date: 2016-06
• Subjects: Bayesian networks -- Air pollution prediction -- Statistical classification -- Multi-label classification; 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.2016.02.030
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classifier for each class variable being predicted; yet this approach overlooks the dependencies among the class variables. By appropriately modeling such dependencies one can improve the accuracy of the forecasts. We address this problem by designing a multi-label classifier, which simultaneously predict multiple air pollution variables. To this end we design a multi-label classifier based on Bayesian networks and learn its structure through structural learning. We present experiments in three different case studies regarding the prediction of PM2.5 and ozone. The multi-label classifier outperforms the independent approach, allowing to take better decisions. Highlights: A multi-label classifier jointly predicts multiple dependent variables. We proposed a Bayesian network multi-label classifier. We learn its structure solving an integer programming problem. We consider the joint prediction of air pollution variables. The multi-label classifier outperforms the usage of multiple independent classifiers.