Mining of rainfall patterns from social media for supporting flood risk management - Andrade, Sidgley Camargo de (2020)
Context. The widespread use of social media platforms and mobile phones in recent years has increased the capability of people to share information anytime, anywhere, and about anything. The past few years have witnessed a growing interest in social media data as a supplementary data source for disaster risk management. Most studies have aimed at extracting spatio-temporal thematic patterns from social media to support the wide range of tasks that comprise disaster risk management. Substantial advances have been made towards the understanding patterns of several natural phenomena, such as floods and earthquakes. Gap. However, scant attention has been given to rain patterns, which are fundamental inputs in many rainfall-runoff models for flood modeling and forecasting, as well as early warning systems of extreme weather. Furthermore, issues such as selection of a representative areal unit of aggregation, temporal validation/calibration with conventional data, and improvement in information retrieval processes have not been thoroughly investigated, and can still be raised as challenges for the establishment of more sophisticated social signals that reflect natural phenomena. Contribution. This doctoral thesis contributes to the extraction of rain patterns from Twitter data for supporting monitoring and forecasting in flood risk management. It advances in establishing (i) a systematic method for the selection of an optimal areal unit, (ii) an approach for the evaluation of the temporal validity of social media activity related to a given phenomenon of interest, (iii) a conceptual specification model for characterization of the spatial units where social signals accurately mirror a given phenomenon of interest, and (iv) a sensitivity analysis of the spatio-temporal patterns of keywords related to a given phenomenon of interest. A series of empirical case studies conducted in Sao Paulo city, Brazil, evaluated such contributions. Results. The results showed the viability of extraction of rain patterns from Twitter data and their potential use to improve the fault tolerance of traditional solutions of flood risk management, especially in areas of lack of conventional data. Conclusions. Social media data can be used as a supplementary data source for rainfall monitoring. Moreover, discussions have provided useful guiding principles to be followed by spatial analysts using social media data as a proxy data source of natural phenomena.