|Home||<< 1 >>|
Flavio Horita, Ricardo Vilela, Renata Martins, Danielle Bressiani, Gilca Palma, & João Porto de Albuquerque. (2018). Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1040–1050). Rochester, NY (USA): Rochester Institute of Technology.
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.