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Abstract: Flood is a critical problem that will increase as a result of climate changes. The problem of flooding is particularly challenging over the rainy season in tropical countries like Brazil. In this context, wireless sensor networks that are capable of sensing and reacting to water levels hold the potential of significantly reducing the damage, health-risks and financial impact of events. In this paper, we aim to outline our experiences with developing wireless sensor network for flood monitoring in Brazil. Our approach is based on Open Geospatial Consortium's (OGC) Sensor Web Enablement (SWE) standards, so as to enable the collected data to be shared in an interoperable and flexible manner. We describe the application of our approach in a real case study in the city of São Carlos/Brazil, emphasizing the challenges involved, the results achieved, and some lessons learned along the way.

Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.

Keywords: Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods

Track: Social Media in Crisis Response and Management

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	Lívia C. Degrossi, Guilherme G. Do Amaral, Eduardo S. M. De Vasconcelos, João Porto De Albuquerque, & Jo Ueyama. (2013). Using wireless sensor networks in the sensor web for flood monitoring in Brazil. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 458–462). KIT; Baden-Baden: Karlsruher Institut fur Technologie. Abstract: Flood is a critical problem that will increase as a result of climate changes. The problem of flooding is particularly challenging over the rainy season in tropical countries like Brazil. In this context, wireless sensor networks that are capable of sensing and reacting to water levels hold the potential of significantly reducing the damage, health-risks and financial impact of events. In this paper, we aim to outline our experiences with developing wireless sensor network for flood monitoring in Brazil. Our approach is based on Open Geospatial Consortium's (OGC) Sensor Web Enablement (SWE) standards, so as to enable the collected data to be shared in an interoperable and flexible manner. We describe the application of our approach in a real case study in the city of São Carlos/Brazil, emphasizing the challenges involved, the results achieved, and some lessons learned along the way. Keywords: Climate change; Health risks; Information services; Information systems; Service oriented architecture (SOA); Water levels; Wireless sensor networks; World Wide Web; Brazil; Critical problems; Financial impacts; Flood monitoring; Open geospatial consortium; Sensor web; Sensor web enablement; Tropical countries; Floods Track: Emergency Management Information Systems Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML
	Benjamin Herfort, João Porto De Albuquerque, Svend-Jonas Schelhorn, & Alexander Zipf. (2014). Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 747–751). University Park, PA: The Pennsylvania State University. Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring. Keywords: Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods Track: Social Media in Crisis Response and Management Share to FB \| Save citation: RTF PDF LaTeX \| Export record: Atom XML MODS XML OAI_DC XML ODF XML SRW_DC XML SRW_MODS XML