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Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Author

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  • Mehrdad Jeihouni

    (University of Tehran)

  • Ara Toomanian

    (University of Tehran)

  • Ali Mansourian

    (University of Tehran
    Lund University)

Abstract

Groundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision tree-based data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

Suggested Citation

  • Mehrdad Jeihouni & Ara Toomanian & Ali Mansourian, 2020. "Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 139-154, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:1:d:10.1007_s11269-019-02447-w
    DOI: 10.1007/s11269-019-02447-w
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    1. Guoqiang Chen & Tianyu Long & Jiangong Xiong & Yun Bai, 2017. "Multiple Random Forests Modelling for Urban Water Consumption Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4715-4729, December.
    2. Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
    3. Seyed Amir Naghibi & Kourosh Ahmadi & Alireza Daneshi, 2017. "Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2761-2775, July.
    4. Shaghayegh Miraki & Sasan Hedayati Zanganeh & Kamran Chapi & Vijay P. Singh & Ataollah Shirzadi & Himan Shahabi & Binh Thai Pham, 2019. "Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 281-302, January.
    5. Anas Mahmood Al-Juboori, 2019. "Generating Monthly Stream Flow Using Nearest River Data: Assessing Different Trees Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3257-3270, July.
    6. G. Robinson & S. Moutari & A. A. Ahmed & G. A. Hamill, 2018. "An Advanced Calibration Method for Image Analysis in Laboratory-Scale Seawater Intrusion Problems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3087-3102, July.
    7. Zohreh Sherafatpour & Abbas Roozbahani & Yousef Hasani, 2019. "Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2277-2299, May.
    8. Daniela Ducci, 1999. "GIS Techniques for Mapping Groundwater Contamination Risk," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 279-294, November.
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    2. Mohammad Zounemat-Kermani & Abdollah Ramezani-Charmahineh & Reza Razavi & Meysam Alizamir & Taha B.M.J. Ouarda, 2020. "Machine Learning and Water Economy: a New Approach to Predicting Dams Water Sales Revenue," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 1893-1911, April.

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