Abstracts
Abstract
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.
Keywords:
- Fluoride,
- Groundwater,
- Artificial Neural network,
- Inverse Distance Weighted,
- Mamundiyar basin
Résumé
Les eaux souterraines du bassin de Mamundiyar (Inde) présentent des problèmes avérés de qualité chimique. Leur teneur en fluorures dépasse toujours les valeurs recommandées. La méthode de la Pondération Inverse à la Distance (PID) a été utilisée pour interpoler spatialement différents paramètres chimiques. Une modélisation par réseaux de neurones artificiels a été ensuite appliquée pour comprendre les corrélations et niveaux de sensibilité de tous les paramètres chimiques par rapport aux fluorures. Il y a un faible degré de corrélation entre les paramètres, le plus grand coefficient étant seulement de 0,37. Les quatre paramètres qui influencent le plus les fluorures sont le pH, les chlorures, les sulfates et le calcium. La teneur en chlorures est le paramètre le plus corrélé aux fluorures.
Mots-clés :
- Fluorures,
- Eaux souterraines,
- Réseaux de neurones artificiels,
- Pondération Inverse à la Distance,
- Bassin de Mamundiyar
Appendices
References
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