Caselli,M., Trizio,L., de Gennaro,G. and Ielpo,P.(2009): A simple feedforward neutral network for the PM10 forecasting: Comparison with a radial basis function network and a multivariate linear regression model. Water Air Soil Pollut., 201, 365-377.

『PM10予報に対する単純フィードフォワード・ニューラル・ネットワーク:放射基底関数ネットワークおよび多変数線形回帰モデルとの比較』


Abstract
 The problem of air pollution is a frequently recurring situation and its management has social and economic considerable effects. Given the interaction of the numerous factors involved in the raising of the atmospheric pollution rates, it should be considered that the relation between the intensity of emission produced by the polluting source and the resulting pollution is not immediate. The aim of this study was to realise and to compare two support decision system (neural networks and multivariate regression model) that, correlating the air quality data with the meteorological information, are able to predict the critical pollution events. The development of a back-propagation neural network is presented to predict the daily PM10 concentration q, 2 and 3 days early. The measurements obtained by the territorial monitoring stations are one of the primary data sources; the forecasting of the major weather parameters available on the website and the forecasting of the Saharan dust obtained by the “Centro Nacional de Supercompactacion(後のoの頭に`)” website, satellite images and back trajectories analysis are used for the weather input data. The results obtained with the neural network were compared with those obtained by a multivariate linear regression model for 1 and 2 days forecasting. The relative root mean square error for both methods shows that the artificial neural networks (ANN) gives more accurate results than the multivariate linear regression model mostly for 1 day forecasting; moreover, the regression model used, in spite of ANN, failed when it had to fit spiked high values of PM10 concentration.

Keywords: PM10; Forecast; Neural network; Multivariate linear regression 』

1. Introduction
2. Application of feedforward neural networks on automatic PM10 data
 2.1. Feedforward neural networks
 2.2. Input data
 2.3. Training and optimisation of the network
3. Results and discussion
 3.1. Comparison between the ANN and a radial basis function network for 1 day forecasting
 3.2. Comparison between the ANN and a multivariate linear regression model
4. Conclusions
References


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