A Multi-layer Perceptron Neural Network to Predict Air Quality through Indicators of Life Quality and Welfare

Abstract : This paper considers the similarity between two measures of air pollution/quality control, on the one hand, and widely used indicators of life quality and welfare, on the other. We have developed a multi-layer perceptron neural network system which is trained to predict the measurements of air quality (emissions of sulphur and nitrogen oxides), using Eurostat data for 34 countries. We used life expectancy, healthy life years, infant mortality, Gross Domestic Product (GDP) and GDP growth rate as a set of inputs. Results were dominated by GDP growth rate and GDP. Obtaining accurate estimates of air quality measures can help in deciding on distinct dimensions to be considered in multidimensional studies of welfare and quality of life.
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Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.395-402, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_51〉
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Kyriaki Kitikidou, Lazaros Iliadis. A Multi-layer Perceptron Neural Network to Predict Air Quality through Indicators of Life Quality and Welfare. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.395-402, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_51〉. 〈hal-01060641〉

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