Surrogate-based optimization methods for coastal aquifer management

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dc.contributor.author Christelis, Vasileios en
dc.contributor.author Χριστέλης, Βασίλειος el
dc.date.accessioned 2021-03-17T07:04:41Z
dc.date.available 2021-03-17T07:04:41Z
dc.date.issued 2021-03-17
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/53074
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.20772
dc.rights Default License
dc.subject Παράκτιοι υδροφορείς el
dc.subject Seawater intrusion en
dc.subject Βελτιστοποίηση αντλήσεων el
dc.subject Pumping optimization en
dc.subject Μετα-μοντέλα el
dc.subject Surrogate models en
dc.subject Βελτιστοποίηση πολλαπλής πιστότητας el
dc.subject Surrogate-based optimization en
dc.subject εξελικτικοί αλγόριθμοι el
dc.subject Multi-fidelity optimization en
dc.title Surrogate-based optimization methods for coastal aquifer management en
dc.title.alternative Μέθοδοι βελτιστοποίησης βασισμένες σε μετα-μοντέλα στην διαχείριση παράκτιων υδροφορέων el
heal.type doctoralThesis
heal.classification Γεωεπιστήμες & Επιστήμες Περιβάλλοντος el
heal.classification Επιστήμες Ηλεκτρονικών Υπολογιστών και Πληροφορικής el
heal.language en
heal.access free
heal.recordProvider ntua el
heal.publicationDate 2021-02-05
heal.abstract In pumping optimization of coastal aquifers, often the objective is to mitigate the phenomenon of saltwater intrusion while satisfying the demands for freshwater extraction. This management task is typically formulated as a nonlinear constrained optimization problem where the individual pumping rates are the decision variables. Evolutionary algorithms are considered highly competent to find a near-optimal solution to this difficult optimization problem, at the expense of thousands of objective function evaluations with the physics-based seawater intrusion model. This approach leads to an impractical computational cost when it is implemented using the high-fidelity but computationally expensive variable density flow and solute transport models. To that end, the present thesis focused on the use of surrogate models or metamodels as a realistic approach to the computationally expensive problems of pumping optimization of coastal aquifers. The aim was to develop new surrogate-based optimization (SBO) methods, that can realistically be applied in real-world coastal aquifer management problems. Radial basis functions and Kriging surrogate models were selected to develop SBO frameworks. Emphasis was given on the development of online SBO methods where the accuracy of the surrogate models was further enhanced in an iterative fashion (infill strategy). The following SBO schemes were developed: (a) A surrogate-assisted evolutionary framework using a pure exploitation infill strategy. (b) A surrogate-assisted evolutionary framework based on a multiple surrogate approach (either selecting the best surrogate model or forming an optimal weighted ensemble of surrogate models) and a pure exploitation infill strategy. (c) An adaptive-recursive optimization framework which balances exploration with exploitation using the metamodels. (d) Multi-fidelity optimization based on seawater intrusion models of different fidelities and co-Kriging models. In overall, results showed that the proposed SBO methods drastically reduced the computational cost of the corresponding optimization based on the variable-density flow and solute transport model. For pumping optimization problems of moderate dimensionality (i.e., 10 decision variables), all methods approached the region of the global optimum while those balancing exploration with exploitation provided near-optimal solutions within just 100 evaluations with the variable density flow and solute transport model. For pumping optimization problems of a larger dimensionality (i.e., 20 decision variables), the performance of the pure exploitation infill strategies, was negatively affected. However, the optimization schemes which balance exploration and exploitation had a consistent performance and located near-optimal solutions within 300 evaluations with the variable density flow and solute transport model. The new SBO method that balances exploration with exploitation, and it was developed in this thesis, performed better or comparable to other published SBO algorithms that have been successfully applied in the water resources optimization literature. The proposed multi-fidelity optimization framework delivered good local solutions by using as few as 21 simulations with the high-fidelity variable density flow and solute transport model and 200 simulations with a low-fidelity sharp interface model for a pumping optimization problem of 10 decision variables. The multi-fidelity optimization method outperformed conventional SBO for such a limited number of simulations with the high-fidelity seawater intrusion model. en
heal.sponsor Η διατριβή υποστηρίχτηκε με υποτροφία από τον Ειδικό Λογαριασμό Κονδυλίων Έρευνας (Ε.Λ.Κ.Ε.) του ΕΜΠ el
heal.advisorName Μαντόγλου, Αριστοτέλης el
heal.committeeMemberName Καρατζάς, Γεώργιος el
heal.committeeMemberName Νάνου-Γιάνναρου, Αικατερίνη el
heal.committeeMemberName Κατσιφαράκης, Κωνσταντίνος el
heal.committeeMemberName Μπαλτάς, Ευάγγελος el
heal.committeeMemberName Ναλμπάντης, Ιωάννης el
heal.committeeMemberName Τσιχριντζής, Βασίλειος el
heal.committeeMemberName Μαντόγλου, Αριστοτέλης el
heal.academicPublisher Σχολή Αγρονόμων και Τοπογράφων Μηχανικών el
heal.academicPublisherID ntua
heal.numberOfPages 166 p. en
heal.fullTextAvailability false

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