Use of social media data in flood monitoring - Restrepo Estrada, Camilo Ernesto (2018)
Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events.