Browse > Article
http://dx.doi.org/10.6109/jkiice.2022.26.3.341

Implementation of CNN-based classification model for flood risk determination  

Cho, Minwoo (Department of Computer Engineering, Paichai University)
Kim, Dongsoo (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.3, 2022 , pp. 341-346 More about this Journal
Abstract
Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.
Keywords
DNN; K-Means Clustering; Time series data; Flood Risk Determination; Flood;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 M. S. Jang, K. W. Nam, and Y. S. Lee, "Analysis and Application of Power Consumption Patterns for Changing the Power Consumption Behaviors," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 4, pp. 603-610, Apr. 2021.   DOI
2 Water environment information system [Internet]. Available: http://water.nier.go.kr/
3 S. T. Hong, J. H. Park, and H. K. Jung, "Network traffic analysis of satellite communication system for hydrologic observation," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 9, pp. 1139-1145, Sep. 2019.   DOI
4 Ministry of Public Administration and Security. 2019 Disaster Yearbook [Internet]. Available: https://www.mois.go.kr/frt/bbs/type001/commonSelectBoardArticle.do;jsessionid=9q+z++-8qP6PFH1L9NfdGfxr.node20?bbsId=BBSMSTR_000000000014&nttId=81886.
5 J. H. Park, K. B. Hwang, H. M. Park, and Y. K. Choi, "Application of CNN for Fish Species Classification," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 1, pp. 39-46, Jun. 2019.   DOI
6 N. A. Maspo, A. N. B. Harun, M. Goto, F. Cheros, N. A. Haron, and M. N. M. Nawi, "Evaluation of Machine Learning approach in flood prediction scenarios and its input parameters: A systematic review," in IOP Conference Series: Earth and Environmental Science, vol. 479, 2020.
7 S. H. Park and H. J. Kim, "Design of Artificial Intelligence Water Level Prediction System for Prediction of River Flood," Journal of the Korea Institute of Information and Communication Engineering, vol. 24, no. 2, pp. 198-203, Feb. 2020.   DOI
8 J. Y. Kim, B. S. Kang, and H. K. Jung, "Determination of coagulant input rate in water purification plant using K-means algorithm and GBR algorithm," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 6, pp. 792-798, Jun. 2021.   DOI
9 M. Pan, H. Zhou, J. Cao, Y. Liu, J. Hao, S. Li, and C. H. Chen, "Water level prediction model based on GRU and CNN," IEEE Access, vol. 8, pp. 60090-60100, Mar. 2020.   DOI