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.