Near real time flood inundation mapping using social media data as an information source: a case study of 2015 Chennai flood

• Main Authors: Dhivya Karmegam, Sivakumar Ramamoorthy, Bagavandas Mappillairaju
• Format: Article
• Language: English
• Published: SpringerOpen 2021-09-01
• Series: Geoenvironmental Disasters
• Subjects: Flood; Inundation map; Social media; Geographical information system
• Online Access: https://doi.org/10.1186/s40677-021-00195-x

Abstract During and just after flash flood, data regarding water extent and inundation will not be available as the traditional data collection methods fail during disasters. Rapid water extent map is vital for disaster responders to identify the areas of immediate need. Real time data available in social networking sites like Twitter and Facebook is a valuable source of information for response and recovery, if handled in an efficient way. This study proposes a method for mining social media content for generating water inundation mapping at the time of flood. The case of 2015 Chennai flood was considered as the disaster event and 95 water height points with geographical coordinates were derived from social media content posted during the flood. 72 points were within Chennai and based on these points water extent map was generated for the Chennai city by interpolation. The water depth map generated from social media information was validated using the field data. The root mean square error between the actual water height data and extracted social media data was ± 0.3 m. The challenge in using social media data is to filter the messages that have water depth related information from the ample amount of messages posted in social media during disasters. Keyword based query was developed and framed in MySQL to filter messages that have location and water height mentions. The query was validated with tweets collected during the floods that hit Mumbai city in July 2019. The validation results confirm that the query reduces the volume of tweets for manual evaluation and in future will aid in mapping the water extent in near real time at the time of floods.