Design and development of customized applications for the analysis, visualizations, and assessment of the evolution of urban areas using artificial intelligence and machine learning methods

Τσαγκής, Παύλος; Tsagkis, Pavlos

dc.contributor.author	Τσαγκής, Παύλος	el
dc.contributor.author	Tsagkis, Pavlos	en
dc.date.accessioned	2025-03-07T07:09:53Z
dc.date.available	2025-03-07T07:09:53Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61234
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.28930
dc.rights	Default License
dc.subject	Αστικά μοντέλα ανάπτυξης	el
dc.subject	Γεωγραφικά πληροφοριακά συστήματα	el
dc.subject	Τεχνητή Νοημοσύνη	el
dc.subject	Μηχανική μάθηση	el
dc.subject	Νευρωνικά δίκτυα	el
dc.subject	Urban growth models	en
dc.subject	Geographical Information Systems (GIS)	en
dc.subject	Artificial Intelligence	en
dc.subject	Machine Learning	en
dc.subject	Neural Networks	en
dc.title	Design and development of customized applications for the analysis, visualizations, and assessment of the evolution of urban areas using artificial intelligence and machine learning methods	en
dc.contributor.department	Τομέας Γεωγραφίας και Περιφερειακού Σχεδιασμού	el
heal.type	doctoralThesis
heal.classification	Ολοκληρωμένα Υποδείγματα Προσομοίωσης της εξέλιξης Αστικών Περιοχών	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-10-16
heal.abstract	A lack of planning and management of urban development can result in unsustainable urban development. In order to achieve this goal, effective tools must be implemented, and urban growth models have proven to be an invaluable tool when addressing this issue. With the assistance of machine learning, the goal of this thesis is to develop an urban growth model that can be applied to any geographical area within the European Union utilizing a neural network approach. By developing machine-readable formats for the collected historical open spatial data using a methodology that involved collecting, organizing, handling, and transforming the open spatial data, it was possible to develop machine-readable formats for the collected historical open spatial data. The impact factors include the social, economic, and biophysical forces, as well as the neighboring and political influences, which requires the transformation of such data into tabular form. Furthermore, the thesis introduces an artificial neural network (ANN) model, coupled with a detailed methodology for its training and evaluation. This involves leveraging a robust analytical software tool, built on Python programming language, to ascertain the optimal weights for the various impact factors integrated into the model. The culmination of this rigorous process extends to making predictions for the year 2030, in which the research outputs and detailed maps for each of the five-case study European Union (EU) metropolitan areas are meticulously presented. It is essential to underline that the study's scope is not confined to the specific case study areas but is broadened by the utilization of pan-European datasets. This strategic approach ensures that the developed model is not only applicable to the immediate study locations but can be seamlessly extended to encompass any European region. The inclusivity of pan-European datasets is facilitated through the incorporation of an open-source utility designed to support the model. This innovative feature significantly enhances the model's versatility and underscores its potential as a valuable tool for urban planning and policy-making across the diverse landscapes of Europe. The broader implication of this research is the empowerment of local policy-makers and urban planners with a powerful instrument. By harnessing this sophisticated model, these stakeholders gain the capacity to analyze a myriad of future development scenarios. This analytical capability, grounded in the empirical insights provided by the model, equips decision-makers to formulate informed strategies for sustainable urban development. In essence, this work stands as a cornerstone in navigating the complexities of urban development, offering not just a retrospective analysis but a forward-looking perspective that can guide and inform decision-making processes, ultimately contributing to more resilient and sustainable urban landscapes.	en
heal.advisorName	Bakogiannis, Efthimios
heal.committeeMemberName	Bakogiannis, Efthimios
heal.committeeMemberName	Asprogerakas, Evangelos
heal.committeeMemberName	Doulamis, Anastasios
heal.committeeMemberName	Nikitas, Alexandros
heal.committeeMemberName	Gemenetzi, Georgia
heal.committeeMemberName	Karantzalos, Konstantinos
heal.committeeMemberName	Chatzichristos, Thomas
heal.committeeMemberName	Μπακογιάννης, Ευθύμιος
heal.committeeMemberName	Ασπρογέρακας, Ευάγγελος
heal.committeeMemberName	Δουλάμης, Αναστάσιος
heal.committeeMemberName	Νικήτας, Αλέξανδρος
heal.committeeMemberName	Χατζηχρήστος, Θωμας
heal.committeeMemberName	Γεμενετζή, Γεωργία
heal.committeeMemberName	Καράντζαλος, Κωνσταντίνος
heal.academicPublisher	Σχολή Αγρονόμων και Τοπογράφων Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	296
heal.fullTextAvailability	false
heal.fullTextAvailability	false