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A New Associative Classification Algorithm for Predicting Groundwater Locations

Author

Listed:
  • Faisal Aburub

    (Department of Management Information Systems, Faculty of Administrative and Financial Sciences, University of Petra, P. O. Box 961343, Amman, Jordan)

  • Wa’el Hadi

    (Department of Computer Information Systems, Faculty of Information Technology, University of Petra, P. O. Box 961343, Amman, Jordan)

Abstract

In this paper, we study the problem of predicting new locations of groundwater in Jordan through the application of a proposed new method, Groundwater Prediction using Associative Classification (GwPAC). We identify features that differentiate locations of groundwater wells according to whether or not they contain water. In addition, we survey intelligent-based methods related to groundwater exploration and management. Three experimental analyses were conducted with the objective to evaluate the capability of data mining algorithms using real groundwater data from the Ministry of Water and Irrigation. In the first experiment, we investigated the performance of GwPAC against three well-known associative classification algorithms, namely CBA, CMAR and FACA. Furthermore, three rule-based algorithms — C4.5, Random Forest and PBC4cip — were investigated in the second experiment; further, so as to generalise the capability of using data mining for solving the groundwater detection problem, four benchmark algorithms — SVMs, NB, KNN and ANNs — were evaluated in the third experiment. From all the experiments, the results indicated that all considered data mining algorithms predict locations of groundwater with acceptable classification rate (all classification accuracies >79%), and can be useful methods when seeking to address the problem of exploring new groundwater locations.

Suggested Citation

  • Faisal Aburub & Wa’el Hadi, 2018. "A New Associative Classification Algorithm for Predicting Groundwater Locations," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-26, December.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:04:n:s0219649218500430
    DOI: 10.1142/S0219649218500430
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    References listed on IDEAS

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    1. Fadi Thabtah & Qazafi Mahmood & Lee McCluskey & Hussein Abdel-Jaber, 2010. "A New Classification Based on Association Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 55-64.
    2. Ali El-Naqa & Ammar Al-Shayeb, 2009. "Groundwater Protection and Management Strategy in Jordan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2379-2394, September.
    3. Neda Abdelhamid & Aladdin Ayesh & Fadi Thabtah & Samad Ahmadi & Wael Hadi, 2012. "MAC: A Multiclass Associative Classification Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-10.
    4. M. Salahat & M. Al-Qinna & K. Mashal & N. Hammouri, 2014. "Identifying Major Factors Controlling Groundwater Quality in Semiarid Area Using Advanced Statistical Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3829-3841, September.
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