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• W3108599956 abstract "Abstract Effective flood risk management strategies require a detailed understanding of the source, extent and impact of flooding. Unmanned Aerial Vehicles (UAVs) enable detailed and accurate data collection that can be used to determine flood source, extent, impact and the presence of property level flood resistance measures. This paper draws on the practical experience of the authors including the use of UAVs during flood events. We highlight the potential uses of UAVs in flood risk management activities and the associated challenges. The impact of a flooding event will also be dependent on how well an area is prepared in terms of community and property level resistance and resilience measures. We have looked at potential reasons why there is not a greater uptake of property level resistance and resilience measures. It is clear that a standardised approach is required if UAVs are to fulfil their potential within flood risk management activities. We have identified five pillars of standardisation that underpin an overarching, purpose-driven, cost-effective systems-based approach to the use of UAVs in flood risk management. These are as follows: (P1) deployment, data collection and flight-related regulatory requirements; (P2) data processing, data merging and outputs; (P3) the introduction and use of innovative approaches and technological integration; (P4) use of outputs for public engagement and (P5) policy development and governance. We consider that the proposed approach will maximise cost-effective information gathering, standardise the way processed outcomes are generated and provide the basis for comparable and robust flood risk information that is based on a single coherent methodology." @default.
• W3108599956 created "2020-12-07" @default.
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• W3108599956 date "2020-12-01" @default.
• W3108599956 modified "2023-09-23" @default.
• W3108599956 title "Towards more effective strategies to reduce property level flood risk: standardising the use of Unmanned Aerial Vehicles" @default.
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• W3108599956 doi "https://doi.org/10.2166/aqua.2020.093" @default.
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