Predictive applications of Australian flood loss models after a temporal and spatial transfer

Nafari, Roozbeh Hasanzadeh; Ngo, Tuan

doi:10.1080/19475705.2018.1445666

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…The second stream of investigation utilizes various forms of floods/inundation/vulnerability modeling to explore the scope of loss, which is treated as a dependent variable. This approach is applied to calculating losses in regards spatial planning and spatial transfer (Hasanzadeh Nafari & Ngo, 2018; Mustafa et al, 2018), rainfall intensity (Grahn & Nyberg, 2017), unintended consequences of structural protection (Auerswald et al, 2019), credit constraints as a factor hindering relocation and increasing vulnerability (Husby et al, 2015), or GHG emission mitigation as a factor leading to flood losses reduction (Wobus et al, 2017). This type of study has reached a high level of technical advancement.…”

Section: Key Findingsmentioning

confidence: 99%

Improving flood resilience through governance strategies: Gauging the state of the art

Hegger

2021

WIREs Water

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There has been an upsurge in studies of flood risk governance (FRG): steering and decision-making by public and private actors as a complement to risk assessments and technical management options. The scholarly debate is, however, highly fragmented, complicating the production of cumulative insights. To address this knowledge gap, we used six governance strategies for achieving flood resilience that previously have been put forward as a conceptual framework to review 121 articles published between 2016 and 2019, complemented with insights contained in recent overview articles, to gauge the state-of-theart in FRG literature: to (a) diversify flood risk management strategies; (b) align the strategies; (c) adequately involve private actors, including citizens; (d) put an adequate rule system in place; (e) cater for sufficient monetary and non-monetary resources; (f) inspire an open and inclusive normative debate.We found, first, that literature is producing insights on increasingly technically advanced risk assessments and agent-based models but societal debate on justice in flood risk governance is getting attention. A clearly emerging topic is that of citizen engagement in flood risk governance. Second, the geographical focus of the studies is still skewed toward the Global North. To make progress in understanding flood risk governance for better resilience more systematic and comparative empirical assessments of flood risk governance in order to derive generalizable lessons while better taking into account the contextspecificity of FRG. Testing flood risk governance solutions against comparative cases, by balancing the geographical scope of research efforts, and enhancing interdisciplinary and transdisciplinary working is a way to deliver knowledge for more resilience.

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Section: Key Findingsmentioning

confidence: 99%

Improving flood resilience through governance strategies: Gauging the state of the art

Hegger

2021

WIREs Water

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“…Even though there is currently no universally trusted system for assessing flood damage in urban areas, most damage models rely on depth-damage curves (also known as stage-damage functions) for simplicity [11]. In order to apply damage models to assess the economic impact of flooding over urban areas, the required floodwater depths across the inundated area are usually obtained from 2D simulations [12].…”

Section: Literature Review and Analysismentioning

confidence: 99%

Flood Depth‒Damage Curves for Spanish Urban Areas

et al. 2020

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Depth‒damage curves, also known as vulnerability curves, are an essential element of many flood damage models. A relevant characteristic of these curves is their applicability limitations in space and time. The reader will find firstly in this paper a review of different damage models and depth‒damage curve developments in the world, particularly in Spain. In the framework of the EU-funded RESCCUE project, site-specific depth‒damage curves for 14 types of property uses have been developed for Barcelona. An expert flood surveyor’s opinion was essential, as the occasional lack of data was made up for by his expertise. In addition, given the lack of national standardization regarding the applicability of depth‒damage curves for flood damage assessments in Spanish urban areas, regional adjustment indices have been derived for transferring the Barcelona curves to other municipalities. Temporal adjustment indices have been performed in order to modify the depth‒damage curves for the damage estimation of future flood events, too. This study attempts to provide nationwide applicability in flood damage reduction studies.

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“…Recent advances in machine learning (ML) techniques have led to significant improvements in flood risk assessment (Wang et al 2015, Lai et al 2016. Artificial neural network (ANN), decision tree, logistic regression, random forest (RF), regression tree, support vector machine are the most widely used ML models for flood risk assessments (Kourgialas and Karatzas 2017, Mojaddadi et al 2017, Gotham et al 2018, Nafari and Ngo 2018, Shafapour Tehrany et al 2019, Terti et al 2019. Table 1S (available online at stacks.iop.org/ ERL/15/024011/mmedia) lists all of the factors used in these studies.…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

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Predictive applications of Australian flood loss models after a temporal and spatial transfer

Cited by 4 publications

References 50 publications

Improving flood resilience through governance strategies: Gauging the state of the art

Improving flood resilience through governance strategies: Gauging the state of the art

Flood Depth‒Damage Curves for Spanish Urban Areas

Leveraging machine learning for predicting flash flood damage in the Southeast US

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