A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding

Pluvial flooding can have devastating effects, both in terms of loss of life and damage. Predicting pluvial floods is difficult and many cities do not have a hydrodynamic model or an early warning system in place. Citizen science and crowdsourcing have the potential for contributing to early warning systems (EWS) and can also provide data for validating flood forecasting models. Although there are increasing applications of citizen science and crowdsourcing in fluvial hydrology, less is known about activities related to pluvial flooding. Hence the aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding. Based on a search in Scopus, the papers were first filtered for relevant content and then classified into four main themes. The first two themes were divided into (i) applications relevant during a flood event, which includes automated street flooding detection using crowdsourced photographs and sensors, analysis of social media, and online and mobile applications for flood reporting; and (ii) applications related to post-flood events. The use of citizen science and crowdsourcing for model development and validation is the third theme while the development of integrated systems is theme four. All four main areas of research have the potential to contribute to EWS and build community resilience. Moreover, developments in one will benefit others, e.g., further developments in flood reporting applications and automated flood detection systems will yield data useful for model validation

Profiling Flood Risk through Crowdsourced Flood Level Reports

Disaster risk reduction and management, which includes flood risk management, is among the top priorities in the Philippines. In the process of contributing to flood monitoring and public awareness, FloodPatrol, an Android mobile phone application, allows the crowd to report flood levels in various locations. However, a crowdsourcing-based approach poses the challenge to the credibility of the crowdsourced data. Towards the goal of having a validation model using crowdsourced flood level reports, this study presents two results. First, that there is a significant difference between No Flood reports and Flood-leveled reports. Second, that there are four possible distinct groups shown in profiling the flood reports by location