Author : Ajinkya P. Chatur 1
Date of Publication :10th November 2017
Abstract: Data analysis is a stream which uses concepts from statistics and computer science to clean the data and then to find some useful information out of it. In order, to access useful information, various algorithms of machine learning can be used. Such algorithms can further be used for prediction of future data to some extent. So this paper is focused on the survey of different methods and algorithms to analyze the patterns of groundwater level. After the analysis of their methods and their accuracies, it is concluded that CBA method can best describe the trends in groundwater and various factors affecting it.
Reference :
-
- Jordan Ministry of Water and Irrigation – Reports 2013-2016. http://www.mwi.gov.jo/sites/enus/SitePages/MWI%20BG R/Reports.asp x
- Nortcliff A, Carr G, Potter RB, Darmame K. (2008) Jordan’s Water Resources: Challenges for the Future. Geographical Paper No. 185, The University of Reading.
- Karthik, D., & Vijayarekha, K. (2014). Multivariate Data Mining Techniques for Assessing Water Potability. Rasayan Journal of Chemistry, 7 (3):256-259.
- Maatta, S. (2011). Predicting groundwater levels using linear regression and neural networks, CS229 final project, December 15, 2011
- Al Kuisi, M., El-Naqa, A., & Hammouri, N. (2006). Vulnerability mapping of shallow groundwater aquifer using SINTACS model in the Jordan Valley area, Jordan. Environmental Geology, 50(5), 651-667.
- Karthik, D., Vijayarekha, K. & Abirami S. (2015). Classifying ground water quality using data mining technique for Thanjavur district, Tamilnadu, India. Journal of Chemical and Pharmaceutical Research, 7(3):1724-1727.
- Liu B., Hsu W. and Ma Y. (1998). Integrating classification and association rule mining. Proceedings of the KDD, (pp. 80-86). New York, NY.
- Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487- 499).
- Antonie M. and Zaiane O. (2002). Text Document Categorization by Term Association, Proceedings of the IEEE International Conference on Data Mining (ICDM '2002), (pp.19-26), Maebashi City, Japan. [
- Vapnik V. (1995). The Nature of Statistical Learning Theory, chapter 5. Springer-Verlag, New York.
- Hadi, W., Thabtah, F., ALHawari, S., & Ababneh, J. (2008). Naive Bayesian and k-nearest neighbour to categorize Arabic text data. In Proceedings of the European Simulation and Modelling Conference. Le Havre, France (pp. 196-200).
- S. Palani, S. Liong, P. Tkalich, “An ANN application for water quality forecasting”, Marine Pollution Bulletin 56 (2008) 1586–1597.