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

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

doi:10.1016/j.compenvurbsys.2012.02.003

Cited by 56 publications

(22 citation statements)

References 17 publications

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“…Risk‐based design of hydraulic structures is an integral part of mitigation planning to reduce flood damage (Heidari 2009). Current flood standards and policies are being re‐examined to account for possible hydrological changes (Danso‐Amoako et al. 2012; England 2011; Gersonius et al.…”

Section: Adaptation Mitigation and Future Risksmentioning

confidence: 99%

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Floods in a Changing Climate

Andersen¹,

Shepherd²

2013

Geography Compass

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Atmospheric warming and associated hydrological changes have implications for regional flood intensity and frequency. Climate models and hydrological models have the ability to integrate various contributing factors and assess potential changes to hydrology at global to local scales through the century. This survey of floods in a changing climate reviews flood projections based on sources of precipitation, ice and snow melt, and coastal inundation. Topographic and anthropogenic influences that exacerbate or reduce flood risks by altering surface runoff, infiltration, storage, and precipitation development are also considered. Flood mitigation and adaptation strategies for infrastructure, agriculture, public health, and local communities are explored along with uncertainties and challenges in flood research. Recent and upcoming datasets to help with future flood monitoring and prediction include satellite missions, advanced radar, and in-situ networks.

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Section: Adaptation Mitigation and Future Risksmentioning

confidence: 99%

“…2011). In the UK, 5000 dams are reported at risk of failure due to embankment subsidence, erosion, and severe rainfall events (Danso‐Amoako et al. 2012).…”

Section: Adaptation Mitigation and Future Risksmentioning

confidence: 99%

Floods in a Changing Climate

Andersen¹,

Shepherd²

2013

Geography Compass

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“…The test of ANN forecast models showed promising results for 5 h lead-time. In another attempt, Danso-Amoako [126] provide a rapid system for predicting flood with ANN. They provide a reliable forecasting tool for rapidly assessing floods.…”

Section: Short-term Flood Prediction With MLmentioning

confidence: 99%

“…Among the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture and the socioeconomic system. Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection and preparedness [1]. Flood prediction models are of significant importance for hazard assessment and extreme events management.…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Kwok-wing³

2018

Preprint

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…• Subjectivity and aggregation are generic limitations of an expert-based system, which can be addressed by involving expert groups and determination of uncertainty values for all estimations [14,29,30]; • Some ecosystem service variables are not always applicable; • Strong perceived (often falsely; see below) bias towards natural sites and "soft" SuDS (e.g., ponds and wetlands) in contrast to urban sites and "hard" SuDS (e.g., permeable pavements and belowground storage systems); and • Possibility of multicollinearity among variables due to potential dependencies between some of them [31]. Notes: P value, probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true; H, response indicator; if H = 1, filters are statistically significantly different (P < 0.05) for the corresponding water quality parameter; if H = 0, the difference is not significant.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Selecting Sustainable Drainage Structures Based on Ecosystem Service Variables Estimated by Different Stakeholder Groups

Scholz

Uzomah

Almuktar

et al. 2013

Water

Self Cite

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In times of recession, expert systems supporting environmental managers undergo a revival. However, the retrofitting of sustainable water structures is currently undertaken ad hoc using engineering experience supported by minimal formal guidance. There is a lack of practical decision tools that can be used by different professions for the rapid assessment of ecosystem services that can be created when retrofitting water structures. Thus the aim was to develop an innovative decision support tool based on the rapid estimation of novel ecosystem service variables at low cost and acceptable uncertainty. The tool proposes the retrofitting of those sustainable drainage systems that obtained the highest ecosystem services score for a specific urban site subject to professional bias. The estimation of variables was undertaken with high confidence and manageable error at low cost. In comparison to common public opinion, statistically significant differences between social scientists and the general public for the estimation of land costs using the non-parametric Mann-Whitney U-test were found. It was also surprising to find no significant differences in the estimation of habitat for species by civil engineers and ecologists. The new methodology may lead to an improvement of the existing urban landscape by promoting ecosystem services.

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

Cited by 56 publications

References 17 publications

Floods in a Changing Climate

Floods in a Changing Climate

Flood Prediction Using Machine Learning, Literature Review

Selecting Sustainable Drainage Structures Based on Ecosystem Service Variables Estimated by Different Stakeholder Groups

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