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Doctoral Thesis
DOI
10.11606/T.18.2019.tde-19032019-143847
Document
Author
Full name
Camilo Ernesto Restrepo Estrada
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2018
Supervisor
Committee
Mendiondo, Eduardo Mario (President)
Álvarez Villa, Oscar David
Cuartas Pineda, Luz Adriana
Delbem, Alexandre Cláudio Botazzo
Martínez Álvarez, Francisco
Title in English
Use of social media data in flood monitoring
Keywords in English
Data assimilation
Data fusion
Ensemble Kalman filter
Flood monitoring
Hydrological modelling
Probability distributed model
Social media
Streamflow estimation
Abstract in English
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.
Title in Portuguese
Uso de dados das mídias sociais no monitoramento de enchentes
Keywords in Portuguese
Probability Distributed Model
Assimilação de dados
Ensemble Kalman filter
Estimação de vazão
Fusão de dados
Mídias sociais
Modelagem hidrológica
Monitoramento de enchentes
Abstract in Portuguese
As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.
 
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Publishing Date
2019-03-26
 
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