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

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.

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Acknowledgements

A previous shorter version of the paper was presented in the 10th world congress of EWRA “Panta Rei” Athens, Greece, 5-9 July 2017.

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Correspondence to Shishir Gaur.

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Gaur, S., Dave, A., Gupta, A. et al. Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout. Water Resour Manage 32, 5067–5079 (2018). https://doi.org/10.1007/s11269-018-2128-9

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Keywords

  • Groundwater modeling
  • Groundwater management
  • Artificial neural network
  • Bagged decision trees
  • Particle swarm optimization