Profiling Flood Risk through Crowdsourced Flood Level Reports

Victorino, John Noel; Estuar, Ma. Regina E.

doi:10.1109/icitcs.2014.7021800

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…Use of crowdsourced photographs to detect flooding Witherow et al, 2018a,b Use of sensors in vehicles to detect flooding Wu and Wang, 2014;De Guzman et al, 2016;Amagsila et al, 2018 Social media such as Twitter Analysis of data from Twitter or similar social media data to detect flood events or map flood extent Holderness and Turpin, 2015;Kiatpanont et al, 2016;Pandey and Natarajan, 2016;Yadav and Rahman, 2016;Albahari and Schultz, 2017;Arthur et al, 2018;Feng and Sester, 2018;Lin et al, 2018;Yang et al, 2018;Bhuvaneswari and Valliyammai, 2019 Online reporting Use of online applications such as Ushahidi or bespoke applications to report flooding Koswatte et al, 2015Koswatte et al, , 2016Koswatte et al, , 2018Ludwig et al, 2015;Naik, 2016;Victorino et al, 2016;Pánek et al, 2017 Mobile apps Use of mobile apps to report flooding Liu et al, 2011;Victorino and Estuar, 2014;Le Coz et al, 2016;Victorino et al, 2016;Middelhoff et al, 2017;Minn et al, 2017;Sahay et al, 2017;Hendricks et al, 2018;Wang et al, 2018;Yabut et al, 2018 Kutija et al, 2014;Blumberg et al, 2015;Yu et al, 2016;Lewis and Silkstone, 2017;Loftis et al, 2017 Integrated systems − Systems for integrating sensors, authoritative data and crowdsourced data for flood management and early warning ...…”

Section: During Flood Events Automated Flood Detectionmentioning

confidence: 99%

A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding

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2019

Front. Earth Sci.

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Pluvial flooding can have devastating effects, both in terms of loss of life and damage. Predicting pluvial floods is difficult and many cities do not have a hydrodynamic model or an early warning system in place. Citizen science and crowdsourcing have the potential for contributing to early warning systems (EWS) and can also provide data for validating flood forecasting models. Although there are increasing applications of citizen science and crowdsourcing in fluvial hydrology, less is known about activities related to pluvial flooding. Hence the aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding. Based on a search in Scopus, the papers were first filtered for relevant content and then classified into four main themes. The first two themes were divided into (i) applications relevant during a flood event, which includes automated street flooding detection using crowdsourced photographs and sensors, analysis of social media, and online and mobile applications for flood reporting; and (ii) applications related to post-flood events. The use of citizen science and crowdsourcing for model development and validation is the third theme while the development of integrated systems is theme four. All four main areas of research have the potential to contribute to EWS and build community resilience. Moreover, developments in one will benefit others, e.g., further developments in flood reporting applications and automated flood detection systems will yield data useful for model validation.

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Section: During Flood Events Automated Flood Detectionmentioning

confidence: 99%

A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding

See

2019

Front. Earth Sci.

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“…Creek Watch (Kim, Robson, Zimmerman, Pierce, & Haber, ) consists of an iPhone App and supporting WWW site framework for waterway management; this project adopted a citizen science approach and was developed in collaboration with local state agencies. FloodPatrol (Victorino & Estuar, ) is an Android App that supports the crowd‐sourced reporting of flood levels. As flood level in this instance is subjective, issues concerning validation models are explored.…”

Section: Related Researchmentioning

confidence: 99%

Supporting participative pre‐flood risk reduction in a UNESCO biosphere

O’Grady

Evans

Eigbogba

et al. 2019

J Flood Risk Management

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Disaster Risk Reduction (DRR) has become the predominant strategy for pre‐emptively countering the havoc threatened by natural hazards, complementing traditional disaster management, and recovery activities. An important component of DRR activities is community involvement, imbuing the community with a sense of ownership of the risk reduction process, and thus increasing resilience to deal with natural hazards. Though the desirability for community engagement is acknowledged, the differing hazards, environments and community contexts, all pose many obstacles to enabling meaningful participation. This paper describes a participative, community‐oriented initiative for DRR in a context of the most common hazard faced by communities worldwide, that of flooding. A novel platform is presented which embraces participatory science principles in facilitating active community engagement in all stages of the flooding lifecycle. It is demonstrated how observations contributed by a community can contribute both to the practical mitigation of the effects of flooding and the calibration of inundation models. The novelty of the platform lies in its emphasis on mitigation activities during the pre‐flood stage, as well as its innovative use of image capture for enabling the safe assessment of water levels by the community.

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“…Crowdsourcing platforms, such as Ushahidi, have been widely used for collecting citizen data in flood affected areas (Heinzelman & Waters, 2010;Mazzoleni et al, 2017;Pánek, Marek, Pászto, & Valůch, 2017;Victorino & Estuar, 2014;Zook, Graham, Shelton, & Gorman, 2010). Data collected by volunteers in an organised way to carry out specific tasks and through different devices in order to create and publish information of interest, are called Citizen Observatories (CO) (Degrossi, de Albuquerque, Fava, & Mendiondo, 2014;Miorandi, Carreras, Gregori, Graham, & Stewart, 2013).…”

Section: Introduction and Scopementioning

confidence: 99%

Flood modelling using synthesised citizen science urban streamflow observations

Fava

Abe

Restrepo-Estrada

et al. 2018

J Flood Risk Management

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The increase in floods and flash floods over the last decades has motivated researchers to develop improved methodologies for flood risk prevention and warning. Flood forecasting models available today have evolved technologically but are subject to limitations due to the lack of data and limited community participation. This paper presents the Hydrological Alert Model with Participatory Basis (HAMPB) model, an approach for integrating water level data reported by citizens, which has the advantage of being inexpensive and potentially highly available, with traditional data to improve flood forecasting. The model assimilates spatiotemporal water levels measured in the field when they are available through a real‐time estimator. We added random perturbations of up to |10| and |15| cm to those data using the Monte Carlo Method to mimic the uncertainty in citizen science data collection. Applying the HAMPB model for urban nested‐scale catchments (0.11 km2 ≤ Area ≤ 21.84 km2) in Brazil shows: (a) significant improvements in flood simulations when field data was assimilated even considering the volunteered data uncertainty; (b) capability to update simulations in more than one point in the semi‐distributed hydrological model by a regionalization method; and (c) flood hazard indexes and their uncertainties show better estimations using field data for updating.

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Profiling Flood Risk through Crowdsourced Flood Level Reports

Cited by 8 publications

References 11 publications

A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding

A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding

Supporting participative pre‐flood risk reduction in a UNESCO biosphere

Flood modelling using synthesised citizen science urban streamflow observations

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