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Utilizing Twitter Data for Early Flood Warning in Thailand
Author(s)
Jitkajornwanich, Kulsawasd
Kongthong, Chanwit
Khongsoontornjaroen, Nattaya
Kaiyasuan, Jeedapa
Lawawirojwong, Siam
Srestasathiern, Panu
Srisonphan, Siwapon
Vateekul, Peerapon
Date Issued
July 2, 2018
Type
Conference Paper
Abstract
Natural 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.
Citation
Proceedings 2018 IEEE International Conference on Big Data Big Data 2018, 5165-5169, 2018