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Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Author

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  • Shishir Gaur
  • Sudheer Ch
  • Didier Graillot
  • B. Chahar
  • D. Kumar

Abstract

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:3:p:927-941
    DOI: 10.1007/s11269-012-0226-7
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    References listed on IDEAS

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    1. J. Sreekanth & Bithin Datta, 2011. "Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3201-3218, October.
    2. Ioannis Trichakis & Ioannis Nikolos & G. Karatzas, 2011. "Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1143-1152, March.
    3. Liang-Cheng Chang & Hone-Jay Chu & Chin-Tsai Hsiao, 2012. "Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1253-1269, March.
    4. S. Rao & B. Thandaveswara & S. Murty Bhallamudi & V. Srinivasulu, 2003. "Optimal Groundwater Management in Deltaic Regions using Simulated Annealing and Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(6), pages 409-428, December.
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