Analysis of Disaster Safety Situation Classification Algorithm Based on Natural Language Processing Using 119 Calls Data


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 10, pp. 317-322, Oct. 2020
https://doi.org/10.3745/KTSDE.2020.9.10.317,   PDF Download:
Keywords: Artificial intelligence, emergency response, Natural Language Processing, Situation Classification, Machine Learning
Abstract

Due to the development of artificial intelligence, it is used as a disaster response support system in the field of disaster. Disasters can occur anywhere, anytime. In the event of a disaster, there are four types of reports: fire, rescue, emergency, and other call. Disaster response according to the 119 call also responds differently depending on the type and situation. In this paper, 1280 data set of 119 calls were tested with 3 classes of SVM, NB, k-NN, DT, SGD, and RF situation classification algorithms using a training data set. Classification performance showed the highest performance of 92% and minimum of 77%. In the future, it is necessary to secure an effective data set by disaster in various fields to study disaster response.


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Cite this article
[IEEE Style]
S. Kwon, Y. Kang, Y. Lee, M. Lee, S. Park, M. Kang, "Analysis of Disaster Safety Situation Classification Algorithm Based on Natural Language Processing Using 119 Calls Data," KIPS Transactions on Software and Data Engineering, vol. 9, no. 10, pp. 317-322, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.10.317.

[ACM Style]
Su-Jeong Kwon, Yun-Hee Kang, Yong-Hak Lee, Min-Ho Lee, Seung-Ho Park, and Myung-Ju Kang. 2020. Analysis of Disaster Safety Situation Classification Algorithm Based on Natural Language Processing Using 119 Calls Data. KIPS Transactions on Software and Data Engineering, 9, 10, (2020), 317-322. DOI: https://doi.org/10.3745/KTSDE.2020.9.10.317.