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Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps

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  • Mohammad Nikoo
  • Najmeh Mahjouri

Abstract

Water quality zoning is an important step for studying and evaluating surface and groundwater quality variations with time and space. It can also provide important information for developing efficient water quality management strategies. Most common methods for water quality zoning do not consider the uncertainties associated with water resources systems. Also, these methods only categorize the water quality monitoring stations into some classes based on existing water quality but do not provide any information about the quality of water which is categorized in a class. In this paper, a new methodology for probabilistic water quality zoning is developed which utilizes the capabilities of Probabilistic Support Vector Machines (PSVMs) and Fuzzy Inference System (FIS). The required data for training the PSVM is generated by using the FIS in a Monte Carlo Analysis. The trained PSVM-based water quality index provides the probabilities of belonging quality of water to different water quality classes. The applicability of the proposed methodology is investigated by applying it to two surface and groundwater resources systems in Iran. Also, for more evaluating the efficiency of the methodology, the results are compared with those obtained from two clustering techniques, namely Fuzzy Clustering Technique (FCT) and Self-Organizing Map (SOM). The results of surface and ground water quality zoning are depicted in maps by utilizing the Geographic Information System (GIS). Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Mohammad Nikoo & Najmeh Mahjouri, 2013. "Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2577-2594, May.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:7:p:2577-2594
    DOI: 10.1007/s11269-013-0304-5
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    References listed on IDEAS

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    1. Jaco Nel & Yongxin Xu & Okke Batelaan & Luc Brendonck, 2009. "Benefit and Implementation of Groundwater Protection Zoning in South Africa," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 2895-2911, November.
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    1. Mehrdad Ghorbani Mooselu & Hamid Amiri & Sama Azadi & Helge Liltved, 2022. "Spatiotemporal assessment of surface water vulnerability to road construction," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 7851-7873, June.
    2. Farshid Rezaei & Rezvane Ghorbani & Najmeh Mahjouri, 2022. "Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5063-5089, October.
    3. Chandrashekhar Bhuiyan & Prashant Kumar Champati Ray, 2017. "Groundwater Quality Zoning in the Perspective of Health Hazards," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 251-267, January.
    4. Donglin Dong & Wenjie Sun & Zhaochang Zhu & Sha Xi & Gang Lin, 2013. "Groundwater Risk Assessment of the Third Aquifer in Tianjin City, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3179-3190, June.
    5. García Nieto, P.J. & García-Gonzalo, E. & Alonso Fernández, J.R. & Díaz Muñiz, C., 2019. "Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)," Ecological Modelling, Elsevier, vol. 404(C), pages 91-102.
    6. Margaret W. Gitau & Jingqiu Chen & Zhao Ma, 2016. "Water Quality Indices as Tools for Decision Making and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2591-2610, June.
    7. V. Gholami & M. R. Khaleghi & S. Pirasteh & Martijn J. Booij, 2022. "Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 451-469, January.

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