Optimization of Groundwater Pumping and River-Aquifer Exchanges for Management of Water Resources

Mayank Bajpai, Shreyansh Mishra (), Shishir Gaur, Anurag Ohri, Hervé Piégay and Didier Graillot
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Mayank Bajpai: Indian Institute of Technology (BHU)
Shreyansh Mishra: Indian Institute of Technology (BHU)
Shishir Gaur: Indian Institute of Technology (BHU)
Anurag Ohri: Indian Institute of Technology (BHU)
Hervé Piégay: Université de Lyon, ENS de Lyon
Didier Graillot: Ecole Nationale Supérieure Des Mines de Saint-Etienne

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 6, No 8, 1863-1878

Abstract: Abstract Multi-objective optimization problems can be solved through Simulation-Optimization (S-O) techniques where the pareto front gives the optimal solutions in the problem domains. During the selection of different modelling methods, optimization techniques and management scenarios, several pareto fronts can be generated. In the present work, an attempt has been made by performing intensive comparisons between different pareto fronts to compare the efficiency and convergence of different S-O models. In this process, groundwater models were developed to simulate the River-Aquifer (R-A) exchanges for the study area as groundwater pumping influences the rate of R-A exchanges and alters the flow dynamics. The developed models were coupled with optimization models and were executed to solve the multi-objective optimization problems based on the maximization of discharge through pumping wells and maximization of groundwater input into the river through R-A exchanges. The distinctive features of the paper include a pareto front comparison where fronts developed by different S-O models were compared and analysed based on various parameters. The results show the dominance of Multi-Objective Particle Swarm Optimization (MOPSO) over other optimization algorithms and concluded that the maximization of pumping rate significantly changes after considering the R-A exchanges-based objective functions. This study concludes that the model domain also alters the output of simulation–optimization. Therefore, model domain and corresponding boundary conditions should be selected carefully for the field application of management models. The artificial neural network (ANN) models have been also developed to deal with the computationally expensive simulation models by reducing the processing time and found efficient.

Keywords: Simulation-optimization; Multi-objective optimization; Artificial neural network; River-Aquifer exchanges (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03112-5

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