Adaptative DNN emulator-enabled multi-objective optimization to manage aquifer−sea flux interactions in a regional coastal aquifer

Xiayang Yu, J. Sreekanth, Tao Cui, Trevor Pickett and Pei Xin

Agricultural Water Management, 2021, vol. 245, issue C

Abstract: This study focuses on the analyses of influx and efflux of groundwater at the aquifer−sea interface in response to the total groundwater extraction from a regional coastal aquifer. The groundwater planning and management goal is formulated as a multi-objective optimization problem to optimize the total pumping, groundwater influx and efflux through the coastal boundary. A four-stage optimization strategy is implemented for solving this optimization problem, whereby the first three stages are the iterative optimizations using the proposed Multi-Objective Particle Swarm Optimization algorithm and the analysis of Pareto-optimal solutions, and the last stage is the selection of one bargaining solution using the Kalai-Smorodinsky approach considering compromises among multiple objectives. In order to improve the efficiency of the simulation-optimization model, Deep Neural Networks emulators are fitted to approximate individual optimization objective, and a novel dynamic sampling strategy is applied to adaptively improve the accuracy of the emulators. This study demonstrates that accurate and efficient emulators can be achieved for the regional coastal aquifer with zone-based pumping rate multipliers from eight bands (Band 1 to Band 8) of increasing distance from the coastal boundary as the decision variables. The results from the Pareto-front suggest that the abstractions of Band 2 (close to the sea) can be reduced to the lower boundary of the rate multiplier (0.5) whereas the abstraction of farther bands can be significantly enhanced. The water influx through the coastal boundary was decreased by 15.69% under the slight compromise of the total pumping and water efflux by the selected compromising pumping pattern in the regional model. The final Pareto-optimal solution set and the compromised solution provide valuable information for the groundwater manager to plan sustainable groundwater use. The proposed simulation-optimization approach used in this study can be applied for a wide range of groundwater management problems.

Keywords: Regional groundwater modelling; Pumping optimization; Deep neural networks; Machine learning; Multi-Objective Particle Swarm Optimization; Kalai−Smorodinsky bargaining solution (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321181

DOI: 10.1016/j.agwat.2020.106571

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