Hydro-Geochemical Attributes Based Classifiers for Groundwater Analysis

Freshwater supply is critical for domestic, agricultural and industrial purposes. A good supply of clean water is normally obtained from surface and groundwater water bodies. Nonetheless, many localities rely heavily on the latter as the main source of their water resource. Therefore, proper mapping, exploitation and conservation of groundwater resources should become a primary focus in the years to come. In this study, the groundwater samples collected from Bamanghati, Odisha were assigned into three classes (excellent, good and bad) based on the guidelines provided by World Health Organization in 1984. These water quality assignments were completed via a combined approach of hydro-geochemical information and artificial neural network for reconstructing a classifier for groundwater analysis. Here, the probabilistic approach and boosted instance selection method were used to remove inconsistencies in the dataset and to determine the classification accuracy, respectively. Finally, the transmuted dataset is used for kernel estimator-based Bayesian and Decision tree (J48) classification approaches. The findings from the present study confirm that the preprocessing task using statistical analysis along with the combined method of hydro-geochemical attributes-based classification approach is encouraging while the decision tree approach is better than the Bayesian neural network classifier in terms of precision, recall, F-measures, and Kappa statistics.