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Title

Computing flood probabilities using Twitter: application to the Houston urban area during Harvey

Authors
Bruneau, Pierrick
Hostache, Renaud
Chini, Marco
Matgen, Patrick
Tamisier, Thomas
Published in 9th International Workshop on Climate Informatics. Paris (France) - 2-4 October 2019 - . 2019
Abstract In this paper, we investigate the conversion of a Twitter corpus into geo-referenced raster cells holding the probability of the associated geographical areas of being flooded. We describe a baseline approach that combines a density ratio function, aggregation using a spatio-temporal Gaussian kernel function, and TFIDF textual features. The features are transformed to probabilities using a logistic regression model. The described method is evaluated on a corpus collected after the floods that followed Hurricane Harvey in the Houston urban area in August-September 2017. The baseline reaches a F1 score of 68%. We highlight research directions likely to improve these initial results.
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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BRANGBOUR, Etienne Alaric et al. Computing flood probabilities using Twitter: application to the Houston urban area during Harvey. In: 9th International Workshop on Climate Informatics. Paris (France). [s.l.] : [s.n.], 2019. https://archive-ouverte.unige.ch/unige:129070

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Deposited on : 2020-01-20

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