Wave Input reduction methods combining numerical modelling & Machine Learning for the prediction of the annual coastal bed morphological evolution

Παπαδημητρίου, Ανδρέας; Papadimitriou, Andreas

dc.contributor.author	Παπαδημητρίου, Ανδρέας	el
dc.contributor.author	Papadimitriou, Andreas	en
dc.date.accessioned	2024-01-31T09:01:51Z
dc.date.available	2024-01-31T09:01:51Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/58728
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.26424
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Σχηματοποίηση κυματικού κλίματος	el
dc.subject	Μορφολογική εξέλιξη παράκτιου πυθμένα	el
dc.subject	Μαθηματική προσομοίωση	el
dc.subject	Τεχνικές μηχανικής μάθησης	el
dc.subject	Wave Input Reduction	en
dc.subject	Wave schematization	en
dc.subject	Morphological Bed Evolution	en
dc.subject	Machine Learning	en
dc.subject	Numerical Modelling	en
dc.title	Wave Input reduction methods combining numerical modelling & Machine Learning for the prediction of the annual coastal bed morphological evolution	en
dc.title	Σχηματοποίηση κυματικού κλίματος με χρήση μαθηματικών μοντέλων και τεχνικών μηχανικής μάθησης για την ετήσια πρόβλεψη της εξέλιξης του παράκτιου πυθμένα	el
heal.type	doctoralThesis
heal.classification	Ακτομηχανική	el
heal.classification	Παράκτια Μηχανική	el
heal.classification	Coastal Engineering	en
heal.classification	Coastal Morphology	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-09-22
heal.abstract	The numerical prediction of coastal bed evolution has been at the forefront of coastal engineering research for several decades. Even though numerical models are capable of resolving in great detail the coastal processes driving the morphological bed evolution, they are associated with staggering computational burden, rendering a long-term prediction a tedious task. The burden further increases considering the vast amount of input wave characteristics that are used to force the models. To counterbalance this, various wave input reduction methods have been developed and employed in coastal engineering practice. Despite their widespread usage, a need to further accelerate the morphological simulations, while simultaneously improving the reliability of the wave input reduction methods was identified. This thesis undertakes a systematic effort to thoroughly evaluate various types of wave input reduction methods and provide practical guidelines on the optimal method selection and configuration. Two new wave binning wave input reduction methods were conceptualized and implemented in the coastal area of Rethymno, in Greece. These methods focus on the elimination of sea-states considered unable to initiate sediment motion, effectively reducing the length of the forcing timeseries. The newly developed methods produced reliable results compared to the widely used energy flux input reduction method but also achieved a significant model run time reduction of up to 62%, compared to a brute force simulation containing the full forcing timeseries. Additionally, the K-Means clustering algorithm was thoroughly evaluated as a viable alternative to the classical binning input reduction methods and several enhancements were proposed aiming to overturn the unsupervised nature of the clustering algorithm. Implementation of the configurations in the coastal area of Rethymno showcased that the default parametrization of the algorithm can produce satisfactory predictions of annual coastal bed evolution. All the proposed enhancements, exhibited a performance increase compared to the default parametrization, however they added a degree of complexity in the algorithm implementation. A systematic evaluation of the Representative Morphological Wave Height selection approaches was also carried out, with the development of three new alternative configurations. Notable was the training and validation of an Artificial Neural Network that is included in one of the methods and is tasked with the elimination of lowly energetic seastates that have little to no effect in the morphological bed evolution. The three proposed enhancements provided a noteworthy performance increase compared to the traditional method, with the best performing one being the method incorporating the Artificial Neural Network. Last but not least, three selected input reduction methods were compared with available field measurements at the coastal area of Eresos, Lesvos, Greece, in an attempt to investigate the reliability of wave input reduction methods in real-life settings. The numerical model forced with three selected input reduction methods reproduced the morphological bed evolution in a very satisfying manner, with the best performing being once again the one incorporating the Artificial Neural Network. This thesis provides a thorough evaluation of wave input reduction methods, testing several configurations and enhancements, validating their use against both benchmark numerical predictions and field measurements. The incorporation of machine learning in wave input reduction can further increase the reliability of model predictions, in tandem with a significant reduction of the required computational effort	en
heal.advisorName	Τσουκαλά, Βασιλική	el
heal.advisorName	Tsoukala, Vasiliki	en
heal.committeeMemberName	Τσουκαλά, Βασιλική	en
heal.committeeMemberName	Καραμπάς, Θεοφάνης	en
heal.committeeMemberName	Μακρόπουλος, Χρήστος	el
heal.committeeMemberName	Κατσαρδή, Βασιλική	el
heal.committeeMemberName	Σαμαράς, Αχιλλέας	el
heal.committeeMemberName	Χονδρός, Μιχάλης	el
heal.committeeMemberName	Tsoukala, Vasiliki	en
heal.committeeMemberName	Karambas, Theofanis	en
heal.committeeMemberName	Makropoulos, Christos	en
heal.committeeMemberName	Katsardi, Vasiliki	en
heal.committeeMemberName	Samaras, Achilleas	en
heal.committeeMemberName	Chondros, Michalis	en
heal.committeeMemberName	Benoit, Michel	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Πολιτικών Μηχανικών. Τομέας Υδατικών Πόρων και Περιβάλλοντος. Εργαστήριο Λιμενικών Έργων	el
heal.academicPublisherID	ntua