期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 88, 期 -, 页码 84-92出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2016.11.017
关键词
PM2.5; Aerosol optical depth; ANFIS; SVM; BPANN; Australia
类别
资金
- Centre for Air Quality & Health Research and Evaluation (National Health and Medical Research Council Centre of Research Excellence)
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. (C) 2016 Elsevier Ltd. All rights reserved.
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