Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data
| Autor: | Chris Rae, Ruo-Qian Wang, Yuan Wang, Huina Mao, Wesley Shaw |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Data collection
010504 meteorology & atmospheric sciences Flood myth Computer science business.industry 0208 environmental biotechnology Big data 02 engineering and technology computer.software_genre Crowdsourcing 01 natural sciences Data science 020801 environmental engineering Flooding (computer networking) Data quality Social media Data mining Computers in Earth Sciences Natural disaster business computer 0105 earth and related environmental sciences Information Systems |
| Zdroj: | Computers & Geosciences. 111:139-147 |
| ISSN: | 0098-3004 |
| DOI: | 10.1016/j.cageo.2017.11.008 |
| Popis: | Hyper-resolution datasets for urban flooding are rare. This problem prevents detailed flooding risk analysis, urban flooding control, and the validation of hyper-resolution numerical models. We employed social media and crowdsourcing data to address this issue. Natural Language Processing and Computer Vision techniques are applied to the data collected from Twitter and MyCoast (a crowdsourcing app). We found these big data based flood monitoring approaches can complement the existing means of flood data collection. The extracted information is validated against precipitation data and road closure reports to examine the data quality. The two data collection approaches are compared and the two data mining methods are discussed. A series of suggestions is given to improve the data collection strategy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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