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Abstract Managing fragile island freshwater resources requires identifying pumping strategies that trade off the financial cost of groundwater supply against controlling the seawater intrusion (SWI) associated with aquifer pumping. In this work, these tradeoffs are investigated through a sensitivity analysis conducted in the context of an optimization formulation of the groundwater management problem, which aims at minimizing the groundwater supply operation cost associated with groundwater pumping and desalination treatment, subject to constraints on SWI control, as quantified by the water table drawdown over the well (∆s), the reduction in freshwater volume (∆FV) in the aquifer, or the salt mass increase (∆SM) in the aquifer. This study focuses on a simplified two‐dimensional model of the San Salvador Island aquifer (Bahamas). Pumping strategies are characterized by the distance of the pumping system from the shoreline (WL), the abstraction screen depth (D) and overall pumping rate (Q), constituting the decision variables of the optimization problem. We investigate the impacts of pumping strategies on the operation cost, ∆s, ∆FVand ∆SM. Findings indicate increasingDor decreasingWLreduces ∆s, ∆FVand ∆SM, thus preserving the aquifer hydrogeologic stability, but also leads to extracting saltier groundwater, thus increasing the water treatment requirements, which have a strong impact on the overall groundwater supply cost. From a financial perspective, groundwater abstraction near the island center and at shallow depths seems the most convenient strategy. However, the analysis of the optimization constraints reveals that strategies where the pumping system approaches the island center tend to cause more severe SWI, highlighting the need to trade off groundwater supply cost against SWI control.
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An Efficient Surrogate-based Multi-objective Optimisation Framework with Novel Sampling Strategy for Sustainable Island Groundwater Management
Abstract. In groundwater pumping optimization (GPO), offline-trained data-driven surrogates can be used to replace numerical-intensive simulators in order to save computing time. The traditional offline training approach involves building surrogates prior to optimization, fitting training datasets that cover the input space uniformly or randomly, which can prove inefficient due to the potential oversampling of low-gradient areas and under-sampling of high-gradient areas. This study proposes an offline machine-learning (ML) algorithm that ranks candidate training points by scoring them based on their distance to the closest training point and on the local gradient of the surrogate estimate and then choosing the highest-rank point. This method is applied to develop surrogates for solving a two-objective GPO problem formulated on a three-dimensional (3D) island aquifer, using hydrogeological conditions representative of San Salvador Island, Bahamas. The objectives are to minimise the supply cost (fOC) resulting from groundwater pumping and desalination and maximise fresh groundwater supply (Qp), subject to constraints on seawater intrusion (SWI) control expressed in terms of aquifer drawdown Δs at pumping locations and aquifer salt mass increase ΔSM. Gaussian Process (GP) is the technique applied to construct surrogates of objectives and constraints, alongside the estimation of uncertainties. Using GP models, it is possible to estimate the probability of “Pareto optimality” for each pumping scheme by Monte Carlo simulation. Pareto optimal pumping schemes (POPS) are then characterized by a probability of occurrence, which can be verified by numerical simulation. The GP training strategy's effectiveness in generating POPS is compared to traditional training approaches, showing that such a strategy can efficiently identify reliable POPS.
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- Award ID(s):
- 2022278
- PAR ID:
- 10561737
- Publisher / Repository:
- EGS
- Date Published:
- Journal Name:
- Advances in Geosciences
- Volume:
- 64
- ISSN:
- 1680-7359
- Page Range / eLocation ID:
- 23 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/adgeo-64-23-2024
