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

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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.

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Abbreviations

C wi :

Well installation cost (euros)

C pn :

Capitalized cost of pipelines (euros)

C p :

Total cost of pumping (euros)

C pE :

Capitalized electricity cost (pumping cost)

C pu :

Cost of pump units (euros)

G t :

Global best value among all particles, gbest

h i :

Minimum water head on the periphery of the i th well (m)

b :

Aquifer thickness (m)

K :

Hydraulic conductivity (m/s)

L i :

Pipe length for each well (m)

N w :

Total number of wells

P t :

Previous best value for each particle, pbest

Q :

Discharge from well (m3/s)

Q i :

Discharge from i th well (m3/s)

r :

The rate of interest (euros/euros/year)

R E :

The cost of the electricity per kilowatt-hour (euros/kwh)

V max :

Maximum velocity (m/s)

W :

Complex discharge function

Ψ :

Stream function (m3/s)

Ω :

Rate of groundwater flow (m3/s)

h :

Groundwater head (m)

Ф :

Discharge potential (m3/s)

ω :

Inertia weight

γ :

Density of the fluid (N/m3)

η :

Combined efficiency of the pump and the prime mover

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Authors and Affiliations

  1. UMR CNRS5600 EVS, Département of Géosciences and Environnent, SPIN, Ecole Nationale Supérieure des Mines, 158-Cours Fauriel, Saint-Etienne, 42023, France

    Shishir Gaur & Didier Graillot

  2. Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India

    Sudheer Ch & B. R. Chahar

  3. Department of Civil Engineering, Indian Institute of Science, Bangalore, 560012, India

    D. Nagesh Kumar

Authors
  1. Shishir Gaur
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  2. Sudheer Ch
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  3. Didier Graillot
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  4. B. R. Chahar
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  5. D. Nagesh Kumar
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Correspondence to Shishir Gaur.

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Gaur, S., Ch, S., Graillot, D. et al. Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources. Water Resour Manage 27, 927–941 (2013). https://doi.org/10.1007/s11269-012-0226-7

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  • DOI: https://doi.org/10.1007/s11269-012-0226-7

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