Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring

Autor(es)

Restrepo-Estrada, Camilo ^[1] ; de Andrade, Sidgley Camargo ^{[2, 3]} ; Abe, Narumi ^[1] ; Fava, Maria Clara ^[1] ; Mendiondo, Eduardo Mario ^[1] ; de Albuquerque, Joao Porto ^{[2, 4]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Sch Engn, Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

^[3] Univ Tecnol Fed Parana, Toledo - Brazil

^[4] Univ Warwick, Ctr Interdisciplinary Methodol, Coventry, W Midlands - England

• Tipo de documento: Artigo Científico
• Fonte: Computers & Geosciences; v. 111, p. 148-158, FEB 2018.
• Citações Web of Science: 14

Resumo

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. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early warning systems. (AU)

• Processo FAPESP: 14/50848-9 - INCT 2014: INCT para Mudanças Climáticas (INCT-MC)
• Beneficiário: Jose Antonio Marengo Orsini
• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Temático
• Processo FAPESP: 17/15413-0 - Fusão de dados de mídias sociais e sensores físicos para o monitoramento de chuvas
• Beneficiário: Sidgley Camargo de Andrade
• Modalidade de apoio: Bolsas no Brasil - Doutorado