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Characterizing the Socio-Economic Driving Forces of Groundwater Abstraction with Artificial Neural Networks and Multivariate Techniques

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  • Said Jalala
  • Azzedine Hani
  • Isam Shahrour

Abstract

Integrated groundwater quantity management in the Middle East region must consider appropriate control measures of the socio economic needs. Hence, there is a need for a better knowledge and understanding of the socio economic variables influencing the groundwater quantity. Gaza Strip was chosen as the study area and real data were collected from twenty five municipalities for the reference year 2001. In this paper, the effective variables have been characterized and prioritized using multi-criteria analysis with artificial neural networks (ANN) and expert opinion and judgment. The selected variables were classified and organized using the multivariate techniques of cluster analysis, factor analysis, principal components and classification analysis. There are significant discrepancies between the results of ANN analysis and expert opinion and judgment in terms of ranking and prioritizing the socio-economic variables. Characterization of the priority effective socio-economic driving forces indicates that water managers and planners can introduce demand-based groundwater management in place of the existing supply-based groundwater management. This ensures the success of undertaking responsive technical, managerial and regulatory measures. Income per capita has the highest priority. Efficiency of revenue collection is not a significant socio-economic factor. The models strengthen the integration of preventive approach into groundwater quantity management. In addition to that, they assist decision makers to better assess the socio economic needs and undertake proactive measures to protect the coastal aquifer. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • Said Jalala & Azzedine Hani & Isam Shahrour, 2011. "Characterizing the Socio-Economic Driving Forces of Groundwater Abstraction with Artificial Neural Networks and Multivariate Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2147-2175, July.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:9:p:2147-2175
    DOI: 10.1007/s11269-011-9800-7
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    References listed on IDEAS

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    1. Misgana Muleta & John Nicklow, 2004. "Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 459-482, October.
    2. van der Vlist, M. J., 1999. "Blue node concept: a regional water management strategy," Agricultural Water Management, Elsevier, vol. 40(2-3), pages 265-273, May.
    3. Chuang-lin Fang & Chao Bao & Jin-chuan Huang, 2007. "Management Implications to Water Resources Constraint Force on Socio-economic System in Rapid Urbanization: A Case Study of the Hexi Corridor, NW China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1613-1633, September.
    4. Bouwer, Herman, 2000. "Integrated water management: emerging issues and challenges," Agricultural Water Management, Elsevier, vol. 45(3), pages 217-228, August.
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    1. Kolin Loveless & Aamir Farooq & Noreddine Ghaffour, 2013. "Collection of Condensate Water: Global Potential and Water Quality Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1351-1361, March.

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