Urban flooding can cause widespread devastation in terms of loss of life and damage to property. As such, monitoring urban flood evolution is crucial in identifying the most affected areas, where emergency response resources should be directed. Flood monitoring through airborne or satellite remote sensing is often limited due to weather conditions and urban topography. In contrast, crowdsourced data is not affected by weather or topography, and they hence offer great potential for urban flood monitoring through real-time information shared by individuals. Despite the benefits, there is no guarantee of quality associated with crowdsourced data, which hampers its usability. In this paper, we present and evaluate two different approaches (binary logistic regression and fuzzy logic) to assess the quality of crowdsourced social media data retrieved from the public Twitter archive. Input variables were constructed based on Twitter metadata and spatiotemporal analysis. Both models were trained and tested using actual flood-related information Tweeted during three consecutive years of flooding in Phetchaburi City, Thailand (2016 to 2018), and produced good results. The fuzzy logic approach is shown to perform better, however its implementation involves significantly more subjectivity. The ability to assess data quality enables the uncertainty associated with crowdsourced social media data to be estimated, which allows this type of data to supplement conventional observations, and hence improve flood management activities.

城市洪水会造成广泛的破坏，造成人员伤亡和财产损失。因此，监测城市洪水演变对于确定受灾最严重的地区至关重要，应急响应资源应该在这些地区使用。由于天气条件和城市地形，通过机载或卫星遥感进行的洪水监测通常受到限制。相比之下，众包数据不受天气或地形的影响，因此通过个人共享的实时信息为城市洪水监测提供了巨大的潜力。尽管有这些好处，但无法保证与众包数据相关的质量，这阻碍了其可用性。在本文中，我们提出并评估了两种不同的方法（二元逻辑回归和模糊逻辑）来评估从公共 Twitter 档案中检索的众包社交媒体数据的质量。输入变量是基于 Twitter 元数据和时空分析构建的。两种模型都使用泰国碧武里市连续三年（2016 年至 2018 年）洪水期间在推特上发布的实际洪水相关信息进行训练和测试，取得了良好的效果。模糊逻辑方法表现得更好，但它的实施涉及更多的主观性。评估数据质量的能力可以估计与众包社交媒体数据相关的不确定性，这使得此类数据可以补充常规观察，从而改善洪水管理活动。