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

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 so...

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Vydáno v:Journal of environmental management Ročník 310; s. 114753
Hlavní autoři: Pang, Min, Du, Erhu, Shoemaker, Christine A., Zheng, Chunmiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 15.05.2022
Témata:
Algorithms
aquifers
China
Computationally demanding constrained optimization
environmental management
Groundwater
groundwater flow
Groundwater management
Land subsidence
memory
Simulation-optimization
space and time
subsidence
Surrogate-based optimization
system optimization
China
Groundwater management
Land subsidence
Surrogate-based optimization
Computationally demanding constrained optimization
Simulation-optimization
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí: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 km2 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. •A new optimization algorithm (DYSOC) is proposed for computationally demanding constraint functions.•DYSOC is the first surrogate-based constrained optimization algorithm for tackling the land subsidence problem.•Parallel DYSOC can achieve the theoretically ideal linear speedup when using multicore processing.•DYSOC reduces the computational cost by over 50% compared with other widely used algorithms in a field application.
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2022.114753