Computing flood probabilities using Twitter: application to the Houston urban area during Harvey
This research addresses the problem of real-time flood detection and mapping for emergency responders and urban planners, offering an incremental approach using social media data.
This paper explores converting Twitter data into geo-referenced raster cells representing flood probabilities. The authors developed a baseline method combining density ratio, spatio-temporal Gaussian kernel aggregation, and TFIDF features, achieving an F1 score of 68% when applied to the Houston area during Hurricane Harvey.
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.