A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques. (February 2017)

• Record Type: Journal Article
• Authors: Yeganeh, Bijan; Hewson, Michael G.; Clifford, Samuel; Knibbs, Luke D.; Morawska, Lidia
• Is Part Of: Environmental modelling & software. Volume 88(2017)
• Page Start: 84
• Page End: 92
• Publication Date: 2017-02
• Subjects: PM2.5 -- Aerosol optical depth -- ANFIS -- SVM -- BPANN -- Australia; 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.2016.11.017
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM2.5 concentration. Our findings demonstrated that the CV-R 2 of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM2.5 concentration in sparsely populated areas. Highlights: We used comprehensive dataset to develop a satellite-based model for estimating the PM2.5 concentration. Representative animations are created to visualize the spatiotemporal variation of the predictors. We applied ANFIS for the first time as a core model to estimate the spatiotemporal variation of PM2.5 concentration. We compared ANFIS with support vector machine and back-propagation artificial neural network. Adaptive model identification technique has been used to identify the optimal predictive model.