Floodplain reconstitution based on data collected via smartphones: a methodological approach to hydrological risk mapping

Ribeiro, Ayane Aparecida da Silva; Oliveira, Gabriel Araujo de; Cirilo, José Almir; Alves, Fellipe Henrique Borba; Batista, Larissa Ferreira David Romão; Melo, Vitor Barbosa

doi:10.1590/2318-0331.252020190179

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…The system uses the SCHISM hydrodynamic model with input data such as atmospheric wind and pressure information, tidal harmonic forecasts, and prevailing ocean current inputs. Hidromapp [11] To georeferenced flood data collection and rearrange floodplains through smartphones.…”

Section: Introductionmentioning

confidence: 99%

An alternative flood measurement approach: Flood Meter

Osman

Rizal

Zaini

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Flash floods commonly occur in urban areas due to a short period of heavy rainfall associated with the large impervious area and inadequate drainage system. An increase in the uncertainty of predicting flash floods, especially due to climate change, reduces the accuracy of the existing flood forecasting system. In such cases, the affected zone’s actual flood level data is required for more effective flood management and incident response. This study aims to develop a mobile application called Flood Meter to identify flood levels in real-time. It measured the flood level based on object scale and images taken by mobile phones. Although the validation using an actual flood event with three different types of mobile phones shows lack of accuracy but by considered the maximum flood level only 0.17 m and the result keep improving when flood level increase, it shows a potentially good agreement compared with manual measurement. The application can become an alternative method to disseminate accurate real-time flood situations as well as flood data collection for future flood risk management.

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Section: Introductionmentioning

confidence: 99%

An alternative flood measurement approach: Flood Meter

Osman

Rizal

Zaini

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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Citizen scientists’ engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam

Tran,

Rutten,

Prajapati

et al. 2024

Environ Monit Assess

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Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals’ attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study’s outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

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