New insights into flood warning reception and emergency response by affected parties

Kreibich, Heidi; Müller, Meike; Schröter, Kai; Thieken, Annegret H.

doi:10.5194/nhess-17-2075-2017

Cited by 46 publications

(42 citation statements)

References 44 publications

(100 reference statements)

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“…Also a shift in the distribution of warning source toward official authorities can be observed from 2002 to 2013. This reflects the considerable increase in private precaution as well as the improvement of warning systems after the 2002 flood (Kreibich et al, ; Thieken, Kienzler, et al, ).…”

Section: Discussionmentioning

confidence: 99%

Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Vogel

Weise

Schröter

et al. 2018

Water Resources Research

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Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in‐)dependencies between the considered variables. The models are learned based on postevent surveys with flood‐affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property‐level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property‐level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.

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Section: Discussionmentioning

confidence: 99%

Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Vogel

Weise

Schröter

et al. 2018

Water Resources Research

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“…Another important concept in flood risk management is flood warning, which is linked to the ability of management agencies to evaluate flood propagation based on infrastructure failure cascades (Kreibich, Müller, Schröter, & Thieken, ; Thirumalaiah & Deo, ). The existing flood warning systems monitor data collected from weather forecasts, observations, and network of sensors to issue warning messages (HCFCD, ; Souza, de Lima Curvello, de Souza, & da Silva, ).…”

Section: Literature Reviewmentioning

confidence: 99%

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

Dong

Farahmand

et al. 2019

Computer aided Civil Eng

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This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision-makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near-real-time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. 668 wileyonlinelibrary.com/journal/mice Comput Aided Civ Inf. 2020;35:668-684.

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“…Flood warning dissemination is one of the important efforts that is being taken seriously by many flood risk management authorities in order to continuous improving their flood warning and forecasting systems through a rigorous engineering and research methodology [4].…”

Section: Related Studymentioning

confidence: 99%

An Intelligent Crisis-Mapping Framework for Flood Prediction

2019

IJRTE

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This paper proposes a new framework for crisis-mapping with flood prediction model based on the crowdsourcing data. Crisis-mapping is still at infancy stage development and offers opportunities for exploration. In fact, the application of the crisis-mapping gives fast information delivery and continuous updates for crisis and emergency evacuation using sensors. However, current crisis-mapping is more to the information dissemination of flood-related information and lack of flood prediction capability. Therefore, this paper applied artificial neural network for flood prediction model in the proposed framework. Sensor data from the crowdsourcing platform can be used to predict the flood-related measures to support continuous flood monitoring. In addition, the proposed framework makes used of the unstructured data from the Twitters to support the flood warnings dissemination to locate flood area with no sensor installation. Based on the results of the experiment, the fitted model from the optimization process gives 90.9% of accuracy performance. The significance of this study is that we provide a new alternative in flood warnings dissemination that can be used to predict and visualized the flood occurrence. This prediction is significant to agencies and authorities to identify the flood risk before its occurrence and crisis-maps can be used as an analytics tool for future city planning

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New insights into flood warning reception and emergency response by affected parties

Cited by 46 publications

References 44 publications

Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models

Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

An Intelligent Crisis-Mapping Framework for Flood Prediction

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