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

arXiv: https://arxiv.org/abs/2012.03731 · HTML: https://mlsanity.com/paper/2012.03731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Multi-Response Heteroscedastic Gaussian Process Models and Their Inference](https://mlsanity.com/paper/2308.15370/) (2023)

## Abstract (arXiv)

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.