Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551576
Title: Classification of SFRB for Greater Manchester and prediction of flood risk
Author: Kalimeris, Nicholas Oliver
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2011
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Abstract:
This thesis aims to provide a rapid screening tool for assessment of Sustainable Flood Retention Basins (SFRB) to predict corresponding dam failure risks and to identify the SFRB types in the Greater Manchester region. According to a report created for the UK Government, climate change will enhance the risk of failure of 5,000 dams in the UK. Traditionally, dams are considered to be safe because they have been built according to high technical standards. However, a great number of dams have been constructed many decades ago and don't meet the current state of the art dam design guidelines. A rapid expert-based assessment method for dam failure of SFRB supported by neural networks model has been presented. Across the Greater Manchester Area 110 SFRB were assessed for flood storage and the corresponding dam failure risk was assessed for all the dams. The dam failure risk can be estimated using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flooding Water Volume. A SFRB can be any of six types. Every type has its own purpose, and this can cause confusion and arguments between stakeholders and planners, so a system to clearly identify the type of SFRB is required. Cluster analysis was used to predict the six types of SFRB for the Greater Manchester region. Only eight essential variables were used to do this, Dam height, Dam Length, Outlet Arrangement and Operation, Maximum Flood Water Volume, Flotsam Cover, Flood Protection, Long Term Water Storage and Drinking Water Provision. These variables that are used are of great importance when characterising SFRB and distinguishing between SFRB types.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.551576  DOI: Not available
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