Friday, February 11, 2011

Forecasting Air Pollution with Artificial Neural Networks

3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece (15 page pdf, Water Air Soil Pollut, 29 Aug 2009)

Key Quotes:
The goal of this study is the construction of models, using ANNs, which give the possibility of forecasting the maximum daily value of an ambient air pollution index for NO2, CO, SO2, and O3, for seven different measuring sites of Greater Athens Area (GAA) and for the next three consecutive days, as well as the daily number of consecutive hours with the pollutants above a threshold concentration.
ANNs are a branch of artificial intelligence developed in the 1950s aiming at imitating the biological brain architecture. They are parallel-distributed systems made of many interconnected nonlinear processing elements (PEs), called neurons
we created two different ANNs. The first one (ANN#1) was trained in order to forecast the daily maximum value of the ERPI (for the pollutants CO, NO2, SO2, and O3) for seven different measuring sites in GAA, at the same time, 3 days ahead. The second one (ANN#2) was trained in order to forecast the number of the hours, during the day, with at least one of the pollutants concentrations (CO, NO2, SO2, and O3) above a threshold according to directives of European Union,
The models ability to predict reliably 3 days ahead, the excesses or non-excesses days (days with the limit value of ERPI50), for the year 2005, according to the values of the success index ranges between 84.6% (Liossia 3-day-ahead prediction) and 92.2% (Patission 1-day-ahead prediction).

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