Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods
This research addresses the challenge of improving flood forecasting for disaster risk reduction by exploring the utility of social media as an independent data source, providing an incremental insight into its limitations.
This paper investigates the feasibility of an entirely independent, self-activating flood monitoring system based solely on social media data. The study found that while social media can aid in early detection of some flood events, it also generates a high number of false positives, leading to the conclusion that social media alerts should complement, rather than replace, existing hydrological models.
Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this. In the case of floods, for instance, data collection from social media can be triggered by a severe weather forecast and/or a flood prediction. By way of contrast, in this paper we explore the possibility of having an entirely independent flood monitoring system which is based completely on social media, and which is completely self-activated. This independence and self-activation would bring increased robustness, as the system would not depend on other mechanisms for forecasting. We observe that social media can indeed help in the early detection of some flood events that would otherwise not be detected until later, albeit at the cost of many false positives. Overall, our experiments suggest that social media signals should only be used to complement existing monitoring systems, and we provide various explanations to support this argument.