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.
Cite
@article{arxiv.2012.03731,
title = {Computing flood probabilities using Twitter: application to the Houston urban area during Harvey},
author = {Etienne Brangbour and Pierrick Bruneau and Stéphane Marchand-Maillet and Renaud Hostache and Marco Chini and Patrick Matgen and Thomas Tamisier},
journal= {arXiv preprint arXiv:2012.03731},
year = {2020}
}
Comments
5 pages, 1 figure. Published in Proceedings of the 9th International Workshop on Climate Informatics: CI 2019