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Wolverhampton Intellectual Repository and E-Theses > E-Theses > E-Theses > An evolutionary AI-based decision support system for urban regeneration planning

Please use this identifier to cite or link to this item: http://hdl.handle.net/2436/114896
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Title: An evolutionary AI-based decision support system for urban regeneration planning
Authors: Yusuf, Syed Adnan
Advisors: Georgakis, Panagiotis
Publisher: University of Wolverhampton
Issue Date: 2010
URI: http://hdl.handle.net/2436/114896
Abstract: The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
Type: Thesis or dissertation
Language: en
Description: A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the Degree of Doctor of Philosophy
Keywords: Artificial intelligence
Evolutionary computation
Genetic algorithms
Urban regeneration
Urban planning
Civil engineering
Fuzzy inference systems
Neural networks
Procedural modelling
Pattern recognition
Appears in Collections: E-Theses

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