An Application of Artificial Neural Networks in Environmental Pollution Forecasting

E. Lungu, M. Oprea, and D. Dunea (Romania)


Artificial neural networks, environmental protection, time series forecastings


The paper presents an application of feed-forward artificial neural networks in air pollution short time forecasting. The time series used in the experiments are measurements of some air pollutants specific to urban regions (e.g. NO2, SO2, PM10, TSP). Several tests were run in order to obtain the best results of the environmental pollution forecasting problem. We focus on the comparisons made between RProp and Quickprop training algorithms in respect with MSE obtained after a fixed number of epochs. The experimental results are similar with a slightly better performance for RProp.

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