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

Said Jalala (), Azzedine Hani () and Isam Shahrour ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2011, vol. 25, issue 9, 2147-2175

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

Keywords: Gaza Strip; Groundwater quantity; Multilayer perceptron network; Socio-economic; Multivariate techniques (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s11269-011-9800-7

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