Flood Inundation Probability Estimation by Integrating Physical and Social Sensing Data: Case Study of 2021 Heavy Rainfall in Henan, China

Abstract

Frequent flooding seriously affects people’s safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the “7·20” Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation.