Ground Water Data Analysis using Data Mining: A Literature Review
P. Tamilarasi1, D. Akila2
1P. Tamilarasi, Ph.D Research Scholar, Department of Computer Science, VELS Institute of Science, Technology & Advanced Studies, Chennai (Tamil Nadu), India.
2D. Akila, Associate Professor, Department of Information Technology, School of Computing Sciences, VELS Institute of Science, Technology & Advanced Studies, Chennai (Tamil Nadu), India.
Manuscript received on 10 February 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 28 April 2019 | PP: 202-205 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10490275C19/19©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: Data mining is the process of discovering patterns from hidden datasets. Data mining tools are most widely used in ground water quality prediction. Most of the agriculture field relies on ground water. The accurate prediction of ground water quality may help in growth of agriculture sector. There are number of techniques for predicting Ground water quality and Ground water levels such as regression analysis, clustering algorithms. There are many classification algorithms used in data mining. The appropriate use of classification algorithm will enhance the prediction of water quality easy and accurate. This paper conducts literature survey on recent researches in this field up to date. The study reviews on the techniques of analysing ground water data to develop proper models for improving the quality and prediction of ground water.
Keywords: Ground Water Quality, Prediction, Data Mining, Classification Techniques, Clustering Algorithms.
Scope of the Article: Data Analytics