Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach - Dar, Imran; Sankar, K.; Dar, Mithas; Majumder, Mrinmoy (Revue des sciences de l’eau / Journal of Water Science)

(untitled) (Université du Québec - INRS-Eau, Terre et Environnement (INRS-ETE), 2012)
The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.