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Water Resources Management

, Volume 30, Issue 3, pp 927–946 | Cite as

Multi-Objective Optimization Model for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Efficiency

  • M. Habibi Davijani
  • M. E. Banihabib
  • A. Nadjafzadeh AnvarEmail author
  • S. R. Hashemi
Article

Abstract

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results were compared. Ultimately, comparing the results gained by PSO and GA algorithms, PSO with an economic and profit growth of 54 % and a 13 % rise in employment relative to the base condition, turned out to be more efficient in this application.

Keywords

Allocation of water resources Job creation Agriculture and industry Two-objective optimization Genetic algorithm Particle swarm optimization algorithm 

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • M. Habibi Davijani
    • 1
  • M. E. Banihabib
    • 2
  • A. Nadjafzadeh Anvar
    • 3
    Email author
  • S. R. Hashemi
    • 4
  1. 1.Water Resources EngineeringTehranIran
  2. 2.Department of Irrigation and DrainageUniversity of Tehran, University College of AbureyhanTehranIran
  3. 3.Department of Civil and Environmental EngineeringPolitecnico di Milano UniversityMilanItaly
  4. 4.Department of Water Resources EngineeringUniversity of BirjandBirjandIran

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