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Forecasting human exposure to PM10 at the national level using an artificial neural network approach
Author(s) -
Antanasijević Davor Z.,
Ristić Mirjana Đ.,
PerićGrujić Aleksandra A.,
Pocajt Viktor V.
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2505
Subject(s) - artificial neural network , principal component analysis , european union , econometrics , regression , statistics , mean absolute error , scarcity , predictive modelling , fraction (chemistry) , mean absolute percentage error , computer science , mathematics , artificial intelligence , mean squared error , economics , chemistry , organic chemistry , economic policy , microeconomics
A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.