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Data for Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring
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Restrepo-Estrada, C., de Andrade, S. C., Abe, N., Fava, M. C., Mendiondo, E. M. and Albuquerque, João Porto de (2017) Data for Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring. [Dataset]
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Official URL: https://wrap.warwick.ac.uk/94300/
Abstract
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.
| Item Type: | Dataset | |||||||||||||||||||||||||||||||||
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| Subjects: | G Geography. Anthropology. Recreation > GB Physical geography H Social Sciences > HM Sociology |
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| Divisions: | Faculty of Social Sciences > Centre for Interdisciplinary Methodologies | |||||||||||||||||||||||||||||||||
| Type of Data: | Geo-social media | |||||||||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Hydrometeorological services, Streamflow, Social media | |||||||||||||||||||||||||||||||||
| Publisher: | University of Warwick, Centre for Interdisciplinary Methodologies | |||||||||||||||||||||||||||||||||
| Official Date: | 7 November 2017 | |||||||||||||||||||||||||||||||||
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| Status: | Not Peer Reviewed | |||||||||||||||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||||||||||||||
| Media of Output (format): | .csv; .dbf; .prj; .shp; .shx | |||||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||||||||
| Description: | The dataset contains: tweets.csv: The georeferenced tweets retrieved by Twitter Streaming API by using crawler-twitter for the administrative boundaries of São Paulo city. 355030819A.csv, 355030820A.csv and 355030825A.csv: The rainfall data (mm) provided each 10 min by the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN). streamflow.csv: The streamflow data (m3/s) provided by the (São Paulo Flood Warning System SAISP). Aricanduva_watershed.shp: The watershed area estimated using ArcGIS and ASTER GDEM covering an area of 82 km2. Brazil.shp: Administrative boundaries of Brazil. Sao_Paulo_city.shp: Administrative boundaries of São Paulo city. |
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| Date of first compliant deposit: | 9 November 2017 | |||||||||||||||||||||||||||||||||
| Date of first compliant Open Access: | 10 November 2017 | |||||||||||||||||||||||||||||||||
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