4.7 Article

Efficient, parallelized global optimization of groundwater pumping in a regional aquifer with land subsidence constraints

Journal

JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 310, Issue -, Pages -

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2022.114753

Keywords

Surrogate-based optimization; Computationally demanding constrained & nbsp;& nbsp;optimization ; Simulation-optimization; Land subsidence; Groundwater management

Funding

  1. National Natural Science Foundation of China [41861124003, NSF CISE 1116298, NSF 1049033]

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This paper introduces a method for designing groundwater exploitation schedules with constraints on land subsidence. It is the first application of a parallelized surrogate-based global optimization algorithm to this problem. The study demonstrates significant computational cost and time advantages of this method in a large region in China.
The design of groundwater exploitation schedules with constraints on pumping-induced land subsidence is a computationally intensive task. Physical process-based groundwater flow and land subsidence simulations are high-dimensional, nonlinear, dynamic and computationally demanding, as they require solving large systems of partial differential equations (PDEs). This work is the first application of a parallelized surrogate-based global optimization algorithm to mitigate land subsidence issues by controlling the pumping schedule of multiple groundwater wellfields over space and time. The application was demonstrated in a 6500 km(2) region in China, involving a large-scale coupled groundwater flow-land subsidence model that is computationally expensive in terms of computational resources, including runtime and CPU memory for one single evaluation. In addition, the optimization problem contains 50 decision variables and up to 13 constraints, which adds to the computational effort, thus an efficient optimization is required. The results show that parallel DYSOC (dynamic search with surrogate-based constrained optimization) can achieve an approximately 100% parallel efficiency when upscaling computing resources. Compared with two other widely used optimization algorithms, DYSOC is 2-6 times faster, achieving computational cost savings of at least 50%. The findings demonstrate that the integration of surrogate constraints and dynamic search process can aid in the exploration and exploitation of the search space and accelerate the search for optimal solutions to complicated problems.

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