Hydro-Geochemical Attributes Based Classifiers for Groundwater Analysis

• Autorzy: Mishra Partha Sarathi , Nandi Debabrata , Sahu Pramod Chandra , Mohanta Kamal Lochan , Edinur Hisham Atan , Sarkar Tanmay , Pati Siddhartha

Identyfikatory

DOI

10.12912/27197050/139412

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data mining; classification; Bayesian artificial neural network; hydro-geochemical information; GIS

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2021
• Tom: Vol. 22, iss. 5
• Strony: 28--39
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Mishra Partha Sarathi

• Department of Computer Science, MSCB University, Odisha, 757003, India

• https://orcid.org/0000-0002-5129-3339

autor

Nandi Debabrata

• Department of Remote Sensing and GIS, MSCB University, Odisha, 757003, India

• https://orcid.org/0000-0003-0526-9693

autor

Sahu Pramod Chandra

• Department of Geology, MPC Autonomous College, 757003, Odisha, India

• https://orcid.org/0000-0002-3555-1350

autor

Mohanta Kamal Lochan

• Department of Physics, ITER, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, 751030, India

• https://orcid.org/0000-0002-9732-2211

autor

Edinur Hisham Atan

• School of Health Sciences, University Sains Malaysia, Kelantan, Malaysia

• https://orcid.org/0000-0002-3379-6063

autor

Sarkar Tanmay

• Department of Food Technology and Bio-chemical Engineering, Jadavpur University, Jadavpur, Kolkata, 700032, India
• Malda Polyechnic, West Bengal State Council of Technical Education, Government of West Bengal, Malda, 732102, India

• https://orcid.org/0000-0003-3869-1604

autor

Pati Siddhartha

• School of Health Sciences, University Sains Malaysia, Kelantan, Malaysia
• Centre of Excellence, Khallikote University, Berhampur, India

• https://orcid.org/0000-0002-8276-0008

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