论文标题
使用Twitter计算洪水概率：在哈维期间申请到休斯顿市区
Computing flood probabilities using Twitter: application to the Houston urban area during Harvey
论文作者
论文摘要
在本文中，我们调查了Twitter语料库转换为地理参考的栅格细胞，这些栅格细胞占据了相关的地理区域的可能性。我们描述了一种基线方法，该方法结合了密度比函数，使用时空高斯内核函数和TFIDF文本特征的聚集。使用逻辑回归模型将功能转换为概率。在2017年8月至9月在休斯顿市区的哈维飓风之后的洪水之后，对所述方法进行了评估。基准的F1得分为68％。我们重点介绍了可能改善这些初始结果的研究方向。
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.
