Forecasting SO2 air pollution in Salamanca, Mexico using a ADALINE

M.G. Cortina, U.S. Mendoza, J.M. Barrón-Adame, D.Andina, A. Vega-Corona

A comparison between a linear regression model and a Non-linear regression model is presented in this work for
forecasting of pollution levels due to SO2 in Salamanca city, Gto. Prediction is performed by means of an Adaptive
Linear Neural Network (ADALINE) and a Generalized Regression Neural Network (GRNN). Prediction experiments
are realized for 1, 12 and 24 hours in advance, and the results for linear regression have been satisfactory. The
performance estimation of both models are determined using the Root Mean Squared Error (RMSE) and Mean
Absolute Error (MAE). Obtained results are compared. The final results indicated that ADALINE outperforms the
past approach using GRNN.

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Submitted by LiuH on Mon, 03/07/2006 - 7:49pm.

Interesting paper to read. You used the ADA ANN algorithm for perdicting So2 polution, does it also imply that other air polutions can also be perdicted by using ADA ANN network to obtain better result than GRNN?

Submitted by LiuH on Wed, 12/07/2006 - 9:08am.

Dear authors,

Thanks for your contribution to IPROMS 2006. If you happen to see this message, please respond the query ASAP. Thank you very much for your cooperation.

Submitted by maria on Wed, 12/07/2006 - 11:02pm.

Not necessarily, up to now alone I have made tests for SO2, but serious very good to extend it. I will take into account your opinion. Thank you

Submitted by graham on Tue, 01/04/2008 - 1:35pm.

Ok, so we know how to measure pollution level now but is this enough? I know that we can find many great applications for this, applications that would actually reduce the air pollution levels... yet I think there is a long way ahead until we can come with something reliable that would make the difference, this is what we should all aim.
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