Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

Ebenezer Danso-Amoako, Miklas Scholz, Nickolas Kalimeris, Qinli Yang, Junming Shao

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by 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 Flood Water Volume. A cross-validation R 2 value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.

Original languageEnglish
Pages (from-to)423-433
Number of pages11
JournalComputers, Environment and Urban Systems
Volume36
Issue number5
DOIs
Publication statusPublished - Sept 2012
Externally publishedYes

Keywords

  • Agglomerative clustering
  • Artificial neural networks
  • Dam safety
  • Flood control
  • Rapid screening tool
  • Spatial distribution map

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • General Environmental Science
  • Urban Studies

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