Data Mining and ANFIS Application to Particulate Matter Air Pollutant Prediction. A Comparative Study

The paper analyzes two artificial intelligence methods for particulate matter air pollutant prediction, namely data mining and adaptive neuro-fuzzy inference system (ANFIS). Both methods provide predictive knowledge under the form of rule base, the first method, data mining, as an explicit rule base, and ANFIS as an internal fuzzy rule base used to perform predictions. In order to determine the optimal number of prediction model inputs, we have perform a correlation analysis between particulate matter and other air pollutants. This operation imposed NO2 and CO concentrations as inputs of the prediction model, together with four values of PM10 concentration (from current hour to three hours ago), the output of the model being the prediction of the next hour PM10 concentration. The two prediction models are investigated through simulation in different structures and configurations using SAS® and MATLAB® respectively. The results are compared in terms of statistical parameters (RMSE, MAPE) and simulation time.

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