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Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

Author

Listed:
  • Shishir Gaur

    (Indian Institute of Technology (BHU))

  • Apurve Dave

    (Indian Institute of Technology (BHU))

  • Anurag Gupta

    (Indian Institute of Technology (BHU))

  • Anurag Ohri

    (Indian Institute of Technology (BHU))

  • Didier Graillot

    (UMR CNRS 5600 EVS, Ecole des Mines de Saint-Etienne (EMSE/SPIN))

  • S. B. Dwivedi

    (Indian Institute of Technology (BHU))

Abstract

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.

Suggested Citation

  • Shishir Gaur & Apurve Dave & Anurag Gupta & Anurag Ohri & Didier Graillot & S. B. Dwivedi, 2018. "Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5067-5079, December.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:15:d:10.1007_s11269-018-2128-9
    DOI: 10.1007/s11269-018-2128-9
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    References listed on IDEAS

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    1. Frank Tsai & Vineet Katiyar & Doug Toy & Robert Goff, 2009. "Conjunctive Management of Large-Scale Pressurized Water Distribution and Groundwater Systems in Semi-Arid Area with Parallel Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(8), pages 1497-1517, June.
    2. George P. Karatzas, 2017. "Developments on Modeling of Groundwater Flow and Contaminant Transport," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3235-3244, August.
    3. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5845-5859, December.
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    2. Vassilios A. Tsihrintzis & Harris Vangelis, 2018. "Water Resources and Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4813-4817, December.
    3. Jie, Pengfei & Zhao, Wanyue & Li, Fating & Wei, Fengjun & Li, Jing, 2020. "Optimizing the pressure drop per unit length of district heating piping networks from an environmental perspective," Energy, Elsevier, vol. 202(C).
    4. Penghui Ma & Yajin Hu & Hansheng Liu & Yuannong Li, 2020. "The Optimum Design Criteria for On-demand Pressurized Microirrigation Network Systems: Optimizing Subunits with Paired Laterals based on the Maximum Size," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3237-3255, August.

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