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Flavio Horita; Ricardo Vilela; Renata Martins; Danielle Bressiani; Gilca Palma; João Porto de Albuquerque |
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Title |
Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil |
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Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Pages |
1040-1050 |
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Keywords |
Crowd sensing data, Weather radar precipitation, Kernel density estimation, Flood management, Collaborative platforms |
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Abstract |
Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events. |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
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Track |
Open Track |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
2176 |
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Author |
Werner Leyh; Maria Clara Fava; Narumi Abe; Camilo Restrepo Estrada; Flavio Horita; Eduardo Mario Mendiondo; João Porto de Albuquerque |
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Title |
SDI-Node to interlink Information, essential for Disaster Preparedness and Management, with other Linked Open Data |
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Conference Article |
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Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
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Abstract |
The idea on Linked Open Data (LOD) for Disaster Management was stimulated by the experience with the integration of heterogeneous environmental data based on well-known OGC based web services. A lot of spatial data is available 'via the Web' – but not ?really on the web': many datasets can be viewed, queried and downloaded via web services, but it is usually not possible to reference an entity within a dataset, like a web page. However, persistent identifiers and deep and reliable linking between datasets and tools are frequently required, beyond file level, to items ?within? files. This becomes possible using Semantic Web (SW) technologies, such as the ?Resource Description Framework? (RDF), and opens possibilities to integrate or aggregate subsets of datasets based on logical criteria. Ontological modelling is used to represent conceptual knowledge. This SW approach is able to handle SPARQL queries considering property relations and ontological models. Disaster related data is multidisciplinary by nature, and comprises data entities from observations, experiments, surveys, simulations, models, and higher-order assemblies, along with associated metadata. The present work with AGORA´s SDI-NODE focuses on connecting dispersed disaster-relevant data to enable easier and faster discovery and access of disaster-related data. The cloud-based geographical information system is hereby explored in 3 ways: Firstly it serves as a reference implementation for the current state of art in SDI; Secondly it serves as praxis relevant use case for disaster relevant data and information management: it is worldwide developed and earlier versions are already used by many countries for their national disaster preparedness – with regard to its ability in rapid and easy mapping and its flexibility to be quickly adapted to unpredictable and fast changing crisis scenarios, and thirdly because it serves already, “partially”, as a SDI-LOD-bridge: The SDI node is composed by underlying components (like GeoServer, GeoNode and GeoNetWork) and some of the supporting communities are already developing different facilities to promote the desired SDI+LOD integration. Thus, the ?LOD-enabled SDI-node? explores LOD related technologies to query, integrate and aggregate, over distributed datasets, at feature-level. Final example: The LOD-enabled SDI-Node is a highly appropriate approach and solution to integrate, track, map, catalog and serve information on the ZIKA VIRUS, the AEDES MOSQUITOES and their environmental conditions. |
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Publisher |
Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
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Language |
English |
Summary Language |
English |
Original Title |
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Edition |
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ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
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Track |
Poster Session |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
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no |
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Call Number |
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Serial |
1433 |
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