Artificial Intelligence Techniques for Flood Risk Management in Urban Environments
| Autor: | Zoran Kapelan, Dragan Savic, W. Sayers, Richard Kellagher |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Optimization
Return period QA75 Technology Engineering Multi-objective GE Flood myth business.industry Flooding (psychology) General Medicine Floods Intervention (law) Flood risk management Work (electrical) Flooding Drainage Optimisation Artificial intelligence Duration (project management) Innovation business Engineering(all) |
| ISSN: | 1877-7058 |
| Popis: | Urban flooding is estimated to cause £270 million pounds worth of damage each year in England and Wales alone. There has, therefore, been a clear need to develop improved methods of identifying intervention strategies to reduce flood risk in urban environments. This paper describes ground-work performed towards evaluating the relative suitability of several algorithms applied to multi-objective optimisation of flood risk intervention strategies in an urban drainage network. An effective methodology is described for reducing an array of return period/duration rainfall files to a minimum, and it is described how this methodology makes possible comparisons of optimisation algorithms. This work has been undertaken as part of a STREAM-IDC EngD project which is a collaborative effort between the University of Exeter, and HR Wallingford. |
| Databáze: | OpenAIRE |
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