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Predicting Groundwater Areas Using Data Mining Techniques: Groundwater in Jordan as Case Study

Authors: Faisal Aburub, Wael Hadi

Abstract:

Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126459

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[1] Jordan Ministry of Water and Irrigation – Reports 2013-2016. http://www.mwi.gov.jo/sites/enus/SitePages/MWI%20BGR/Reports.aspx
[2] 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.
[3] Karthik, D., & Vijayarekha, K. (2014). Multivariate Data Mining Techniques for Assessing Water Potability. Rasayan Journal of Chemistry, 7 (3):256-259.
[4] Maatta, S. (2011). Predicting groundwater levels using linear regression and neural networks, CS229 final project, December 15, 2011.
[5] 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.
[6] Salah, H., (2009). Geostatistical analysis of groundwater levels in the south Al Jabal Al Akhdar area using GIS. GIS Ostrava.
[7] Kumar, S., Dirmeyer, P. A., Merwade, V., DelSole, T., Adams, J. M., & Niyogi, D. (2013). Land use/cover change impacts in CMIP5 climate simulations: A new methodology and 21st century challenges. Journal of Geophysical Research: Atmospheres, 118(12), 6337-6353.
[8] Cook, J.B., Roehl, E.A. and Daamen, R.C., 2013. Predicting the Impact of Climate Change on Salinity Intrusions in Coastal South Carolina and Georgia. Proceedings of the 2013 Georgia Water Resources Conference, held April 10–11, 2013, at the University of Georgia.
[9] 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.
[10] Liu B., Hsu W. and Ma Y. (1998). Integrating classification and association rule mining. Proceedings of the KDD, (pp. 80-86). New York, NY.
[11] 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).
[12] 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.
[13] Vapnik V. (1995). The Nature of Statistical Learning Theory, chapter 5. Springer-Verlag, New York.
[14] 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).
[15] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
[16] Hadi, W. (2015). ECAR: A New Enhanced Class Association Rule. Advances in Computational Sciences and Technology, 8(1), 43-52.
[17] Abdelhamid, N., Ayesh, A., & Hadi, W. (2014). Multi-label rules algorithm based associative classification. Parallel Processing Letters, 24 (1), 1450001-1-1450001-21.
[18] Hadi, W. (2013). EMCAR: Expert Multi Class Based on Association Rule. International Journal of Modern Education and Computer Science, 5(3), 33-41.
[19] Thabtah, F., Hadi, W., Abdelhamid, N., & Issa, A. (2011). Prediction Phase in Associative Classification Mining. International Journal of Software Engineering and Knowledge Engineering, 21(06), 855-876.
[20] Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press.