Short-term heavy rainfalls have become the norm as the climate changes. However, previous city drainage design is often unable to cope with this rainfall pattern. Thus, the damage caused by urban flood disasters is high, especially because of relatively high population densities. Several sudden rains in Taiwan have caused flooding in the cities in recent years. Taichung's Shalu area experienced flooding because of the heavy rains brought by typhoons Sura, Tam Mei, and Su Li from 2012 to 2014. The Taichung City Government has made flood remediation investments and has built wireless sensing devices to convey real-time flooding information. However, a considerable amount of resources are required to establish a complete monitoring network, and such requirement is unachievable in practice. Therefore, how to use innovative tools to enhance cities' flood adaptability has become an important issue. As a result of the popularity of smart phones, an increasing number of people publish personal information on popular Internet communities, such as Facebook, Flickr, Twitter, and Plurk.Using their smart devices, people can take pictures and post information to share with community members. The shared information is tagged with coordinate points. Information from a large group of people can thus be screened and integrated for use as valuable flood information. This study investigates the online descriptions of direct experiences during flooding events to obtain the spatial information of floods through semantic retrieval and filtering analysis and thereby identify flood patterns. Sensed disaster data, credible information extracted from Internet communities, and the use of such information as city flooding information can effectively support the assumptions and limitations of physical sensing mathematical models, particularly in terms of the degree of effective operation and adequate maintenance of urban drainage systems and grid mode homogenization. This study, which on urban flooding events in history, extracts real-time flood-related information from online communities, such as Facebook, and compares the actual values of the flood for spatial analysis. Moreover, this study filters VGI on the basis of semantic meaning and obtains 49 "rain"-related descriptions. The descriptions are matched to the flood spatial information reconstructed with FLO-2D simulation, with the result indicating that the distribution correlation is significant. The method of converting human-sensed non-structured information from Internet communities into usable spatial information, extracting usable information available in online communities, converting this information into disaster prevention information, and using physical-sensing FLO-2D simulation to reconstruct flood spatial information for a correlation analysis is unique and innovative. Results show that a non-task-based, non-specific community can compensate for the insufficiency of detective equipment and further provides flood information. Rainfall situations in other areas can be detected with this method and be framed using Facebook's community check-in information to detect possible flooding ranges. Using flood information from online communities to provide initial flood information and to govern cities with broad areas is a feasible method.