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

Gaur, Shishir and Ch, Sudheer and Graillot, Didier and Chahar, BR and Kumar, Nagesh D (2013) Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources. In: WATER RESOURCES MANAGEMENT, 27 (3). pp. 927-941.

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Official URL: http://dx.doi.org/10.1007/s11269-012-0226-7

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.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to SPRINGER, NETHERLANDS
Keywords: Groundwater modeling;Groundwater management;Artificial neural network;Analytic element method;Particle swarm optimization
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Depositing User: Francis Jayakanth
Date Deposited: 06 Mar 2013 11:06
Last Modified: 06 Mar 2013 11:06
URI: http://eprints.iisc.ac.in/id/eprint/45949

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