Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.684089
Title: Artificial intelligence techniques for flood risk management in urban environments
Author: Sayers, William Keith Paul
ISNI:       0000 0004 5919 9798
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2015
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Abstract:
Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.
Supervisor: Savic, Dragan ; Kapelan, Zoran Sponsor: EPSRC ; STREAM-IDC
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.684089  DOI: Not available
Keywords: Flooding ; Flood risk ; Optimisation ; Artificial Intelligence ; Machine Learning ; Hydraulic Optimisation ; Multi-objective optimisation ; Urban flood risk ; LEMMO ; NSGA2 ; Learnable Evolution Model for Multiobjective Optimization
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