Hydro-Geochemical Attributes Based Classifiers for Groundwater Analysis
Partha Sarathi Mishra 1  
,   Debabrata Nandi 2  
,   Pramod Chandra Sahu 3  
,   Mohanta Kamal Lochan 4
,   Hisham Atan Edinur 5  
,   Tanmay Sarkar 6, 7  
,   Siddhartha Pati 5, 8  
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Department of Computer Science, MSCB University, Odisha, 757003, India
Department of Remote Sensing and GIS, MSCB University, Odisha, 757003, India
Department of Geology, MPC Autonomous College, 757003, Odisha, India
Department of Physics, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, 751030
School of Health Sciences, University Sains Malaysia, Kelantan, Malaysia
Malda Polyechnic, West Bengal State Council of Technical Education, Government of West Bengal, Malda, 732102, India
Department of Food Technology and Bio-chemical Engineering, Jadavpur University, Jadavpur, Kolkata, 700032, India
Centre of Excellence, Khallikote University, Berhampur, India
Debabrata Nandi   

Department of Remote Sensing and GIS, MSCB University, Odisha, 757003, India
Publication date: 2021-09-01
Ecol. Eng. Environ. Technol. 2021; 5:28–39
Freshwater supply is critical for domestic, agriculture 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 later as the main source of their water resource. Therefore, proper mapping, exploitation and conservation of groundwater resources should become a primary focus in years to come. In this study, groundwater samples collected from Bamanghati, Odisha were assigned into three classes (excellent, good and bad) based on 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. 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.