Optimal management of a freshwater lens in a small Island using surrogate models and evolutionary algorithms

Behzad Ataie-Ashtiani, Hamed Ketabchi, Mohammad Mahdi Rajabi

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

This paper examines a linked simulation-optimization procedure based on the combined application of an artificial neural network (ANN) and genetic algorithm (GA) with the aim of developing an efficient model for the multiobjective management of groundwater lenses in small islands. The simulation-optimization methodology is applied to a real aquifer in Kish Island of the Persian Gulf to determine the optimal groundwater-extraction while protecting the freshwater lens from seawater intrusion. The initial simulations are based on the application of SUTRA, a variable-density groundwater numerical model. The numerical model parameters are calibrated through automated parameter estimation. To make the optimization process computationally feasible, the numerical model is subsequently replaced by a trained ANN model as an approximate simulator. Even with a moderate number of input data sets based on the numerical simulations, the ANN metamodel can be efficiently trained. The ANN model is subsequently linked with GA to identify the nondominated or Pareto-optimal solutions. To provide flexibility in the implementation of the management plan, the model is built upon optimizing extraction from a number of zones instead of point-well locations. Two issues are of particular interest to the research reported in this paper are: (1) how the general idea of minimizing seawater intrusion can be effectively represented by objective functions within the framework of the simulation-optimization paradigm, and (2) the implications of applying the methodology to a real-world small-island groundwater lens. Four different models have been compared within the framework of multiobjective optimization, including (1) minimization of maximum salinity at observation wells, (2) minimization of the root mean square (RMS) change in concentrations over the planning period, (3) minimization of the arithmetic mean, and (4) minimization of the trimmed arithmetic mean of concentration in the observation wells. The latter model can provide a more effective framework to incorporate the general objective of minimizing seawater intrusion. This paper shows that integration of the latest innovative tools can provide the ability to solve complex real-world optimization problems in an effective way.

Original languageEnglish
Pages (from-to)339-354
Number of pages16
JournalJournal Of Hydrologic Engineering
Volume19
Issue number2
Early online date27 Feb 2013
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • Artificial neural networks
  • Freshwater lens
  • Genetic algorithm
  • Multiobjective analysis
  • Numerical modeling
  • Persian Gulf.
  • Seawater intrusion

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