Quality Assessment of Ground Water on Small Dataset
Aiswarya Vijayakumar1, A. S Mahesh2
1Aiswarya Vijayakumar, Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
2A.S Mahesh, Department of Computer Science and IT, Amrita School of Arts and Sciences, Kochi, Amrita Vishwa Vidyapeetham, Coimbatore (Tamil Nadu), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 475-478 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3134038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Quality assessment of water has a lot of attractions during recent years. Diverse kinds of classification and monitoring techniques were used in this field of study. The present examination investigates the quality of ground water in Kudankulam which is situated Tirunelveli district of Tamil Nadu. A total of 19 samples was accumulated in this region typically from the coastal area during 2011-2012.The evaluation was done on the basis chemical parameters of each samples. This paper explores various classifier models such as KNN, NB and SVM to achieve prediction of groundwater quality. The classification is done based on the Water Quality Index (WQI) of each sample. A near investigation of characterization systems was done dependent on the confusion matrix, accuracy, f1 score, precision and recall. The outcomes propose that SVM is a better method having high accuracy rate than other models.
Keyword: Classification Algorithms, Water Quality Index, Support Vector Machine, Naïve Bayes, K-Nearest Neighbors.
Scope of the Article: Classification