An Efficient Surrogate-based Multi-objective Optimisation Framework with Novel Sampling Strategy for Sustainable Island Groundwater Management
Abstract. f OC) resulting from groundwater pumping and desalination and maximise fresh groundwater supply (Q p), 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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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An Efficient Surrogate-based Multi-objective Optimisation Framework with Novel Sampling Strategy for Sustainable Island Groundwater Management ; volume:64 ; year:2024 ; pages:23-26 ; extent:4
Advances in geosciences ; 64 (2024), 23-26 (gesamt 4)
- Creator
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Yu, Weijiang
Baù, Domenico
Mayer, Alex S.
Geranmehr, Mohammadali
- DOI
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10.5194/adgeo-64-23-2024
- URN
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urn:nbn:de:101:1-2412200705084.085984850909
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:21 AM CEST
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Associated
- Yu, Weijiang
- Baù, Domenico
- Mayer, Alex S.
- Geranmehr, Mohammadali
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