Utilizing Twitter Data for Early Flood Warning in Thailand

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dc.contributor.authorJitkajornwanich, Kulsawasd
dc.contributor.authorKongthong, Chanwit
dc.contributor.authorKhongsoontornjaroen, Nattaya
dc.contributor.authorKaiyasuan, Jeedapa
dc.contributor.authorLawawirojwong, Siam
dc.contributor.authorSrestasathiern, Panu
dc.contributor.authorSrisonphan, Siwapon
dc.contributor.authorVateekul, Peerapon
dc.date.accessioned2026-08-06T10:20:31Z
dc.date.available2026-08-06T10:20:31Z
dc.date.issued2018-07-02
dc.description.abstractNatural disasters cause significant damage to the country as well as its citizens as we have seen in the news. Drought, wild fire, earthquake and flooding are some examples of the primary natural disasters occurred in Thailand. In this research, we focus on »flooding» and use data from Twitter, where users' mobile devices are utilized as IoT input channels. The goal of this work is to analyze near real-time data (tweets) for early flood warning. Traditional methods in processing, analyzing and reporting a flooding event take quite some time. In social medias (through cellphones), on the other hand, by harvesting crowdsources, potential flooding can be predicted faster - though with the price of reliability of the retrieved tweets. In our research, several techniques are incorporated in order to maximize the accuracy of results, including, tokenization, geo-encoding and decoding, NLP via string matching (Levenshtein's algorithms), and Google APIs for visualization. Finally, the dynamic yet user-friendly map is produced with respect to the posted relevant tweets along their associated frequencies.
dc.identifier.citationProceedings 2018 IEEE International Conference on Big Data Big Data 2018, 5165-5169, 2018
dc.identifier.doi10.1109/BigData.2018.8621961
dc.identifier.other2-s2.0-85062639795
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/8741
dc.sourceProceedings 2018 IEEE International Conference on Big Data Big Data 2018
dc.subjectflood warning
dc.subjectgeo-encoding and decoding
dc.subjectgeocoding API
dc.subjectstring matching
dc.subjecttwitter analysis
dc.titleUtilizing Twitter Data for Early Flood Warning in Thailand
dc.typeConference Paper

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