과제정보
이 논문은 행정안전부 재난피해 복구역량강화 기술개발사업의 지원을 받아 수행된 연구임(2021-MOIS36-002).
참고문헌
- Baydargil, H.B., Serdaroglu, S., Park, J.S., Park, K.H., and Shin, H.S. (2018). "Flood detection and control using deep convolutional encoder-decoder architecture." 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT), IEEE, pp. 1-3.
- Bhadra, T., Chouhan, A., Chutia, D., Bhowmick, A., and Raju, P.L.N. (2020). "Flood detection using multispectral images and SAR data." International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, Springer, Singapore, pp. 294-303.
- Choi, J., Park, K., Choi, S., and Jun, H. (2018). "A forecasting and alarm system for reducing damage from inland inundation in coastal urban areas: A case study of Yeosu City." Journal of the Korean Society of Hazard Mitigation, Vol. 18, No. 7, pp. 475-484. https://doi.org/10.9798/KOSHAM.2018.18.7.475
- Dias, D., and Dias, U. (2018). "Flood detection from social multimedia and satellite images using ensemble and transfer learning with CNN architectures." Proceedings of the MediaEval 2018 Workshop, CEUR, France, Vol. 2283, pp. 3-5.
-
Godoy, D. (2018) Understanding binary cross-entropy/log loss: A visual explanation, accessed 13 September 2021,
- Hwang, S., Kim, K., Lee, G., and Lee, M. (2016). "A study on the development of automated damage estimation system using high resolution satellite imagery." Journal of Korean Society of Hazard Mitigation, Vol. 16, No. 2, pp. 161-172. https://doi.org/10.9798/KOSHAM.2016.16.2.161
- Jain, P., Schoen-Phelan, B., and Ross, R. (2020). "Automatic flood detection in SentineI-2 images using deep convolutional neural networks." Proceedings of the 35th Annual ACM Symposium on Applied Computing, ACM, NY, U.S., pp. 617-623.
- Jang, Y., and Chung, D. (2019). "Technology trend for image analysis based on deep learning." Current Industrial and Technological Trends in Aerospace, Vol. 17, No. 1, pp. 113-122.
- Jeon, H., Kim, J., Kim, K., and Hong, I. (2016). "Application of the LISFLOOD-FP model for flood stage prediction on the lower Mankyung River." Journal of Korea Water Resources Association, Vol. 49, No. 6, pp. 459-467. https://doi.org/10.3741/JKWRA.2016.49.6.459
- Kang, J., and Kwak, S. (2014). "Loitering detection solution for CCTV security system." Journal of Korea Multimedia Society, Vol. 17, No. 1, pp. 15-25. https://doi.org/10.9717/KMMS.2014.17.1.015
- Kim, S., Lee, S., Kim, T.W., and Kim, D. (2019). "Estimation of flooded area using satellite imagery and DSM terrain data." Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 7, pp. 471-483. https://doi.org/10.9798/kosham.2019.19.7.471
- Le, X.H., Ho, H.V., Lee, G., and Jung, S. (2019). "Application of long short-term memory (LSTM) neural network for flood forecasting." Water, Vol. 11, No. 7, 1387. https://doi.org/10.3390/w11071387
- LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). "Gradient-based learning applied to document recognition." Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324. https://doi.org/10.1109/5.726791
- Lee, B.J. (2017). "Analysis on inundation characteristics for flood impact forecasting in Gangnam drainage basin." Atmosphere, Vol. 27, No. 2, pp. 189-197. https://doi.org/10.14191/Atmos.2017.27.2.189
- Lee, J., Kang, B., Lee, H., Chung, Y., and Park, D. (2011). "A suspected criminal face identification via SVDD and SRC in surveillance system." Journal of KIISE: Computing Practices and Letters, Vol. 17, No. 2, pp. 135-139.
- Lee, S.H., Kang, D.H., and Kim, B.S. (2018). "A study on the method of calculating the threshold rainfall for rainfall impact forecasting." Journal of the Korean Society of Hazard Mitigation, Vol. 18, No. 7, pp. 93-102. https://doi.org/10.9798/kosham.2018.18.7.93
- Mason, D.C., Speck, R., Devereux, B., Schumann, G.J.P., Neal, J.C., and Bates, P.D. (2009). "Flood detection in urban areas using TerraSAR-X." IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 2, pp. 882-894. https://doi.org/10.1109/TGRS.2009.2029236
- Oga, T., Harakawa, R., Minewaki, S., Umeki, Y., Matsuda, Y., and Iwahashi, M. (2020). "River state classification combining patch-based processing and CNN." Plos One, Vol. 15, No. 12, e0243073. https://doi.org/10.1371/journal.pone.0243073
- Park, H.J. (2013). "A study on monitoring system for an abnormal behaviors by object's tracking." Journal of Digital Contents Society, Vol. 14, No. 4, pp. 589-596. https://doi.org/10.9728/DCS.2013.14.4.589
- Park, J., Dao, D.A., Kim, S., and Kim, D. (2019). "Detection of flood areas using Sentinel-1 satellite imagery and evaluation of its applicability." Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 6, pp. 53-61. https://doi.org/10.9798/kosham.2019.19.6.53
- Park, S.H., and Kim, H.J. (2020). "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. https://doi.org/10.6109/JKIICE.2020.24.2.198
- Ruder, S. (2016). "An overview of gradient descent optimization algorithms." arXiv preprint arXiv:1609.04747.
- Shin, D.H., Baek, J.W., Park, R.C., and Chung, K. (2021). "Deep learning-based vehicle anomaly detection using road CCTV data." Journal of the Korea Convergence Society, Vol. 12, No. 2, pp. 1-6. https://doi.org/10.15207/JKCS.2021.12.2.001
- Shin, H.J., Chae, H.S., Hwang, E.H., and Park, J.Y. (2012). "A study of informationization technique for detecting flood inundation area using RS." Journal of the Korean Association of Geographic Information Studies, Vol. 15, No. 1, pp. 172-183. https://doi.org/10.11108/KAGIS.2012.15.1.172
- Simonyan, K., and Zisserman, A. (2014). "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556.
- Singh, M.S.A.D.J., and Varnica, B. (2014). "Web crawler: Extracting the web data." International Journal of Computer Trends and Technology, Vol. 13, No. 3, pp. 132-137. https://doi.org/10.14445/22312803/IJCTT-V13P128
- Song, Y.H., Song, Y.S., Park, M.J., and Lee, J.H. (2014). "Flood forecasting estimation methodology of standard rainfall for urban mid and small rivers considering upper-and down-stream water levels." Journal of the Korean Society of Hazard Mitigation, Vol. 14, No. 2, pp. 289-298. https://doi.org/10.9798/KOSHAM.2014.14.2.289
- Theera-Umpon, N., Auephanwiriyakul, S., Suteepohnwiroj, S., Pahasha, J., and Wantanajittikul, K. (2008). "River basin flood prediction using support vector machines." 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE, Hong Kong, China, pp. 3039-3043.
- Tsakiri, K., Marsellos, A., and Kapetanakis, S. (2018). "Artificial neural network and multiple linear regression for flood prediction in Mohawk River." Water, Vol. 10, No. 9, 1158. https://doi.org/10.3390/w10091158
- Wu, C.L., and Chau, K.W. (2006). "A flood forecasting neural network model with genetic algorithm." International Journal of Environment and Pollution, Vol. 28, No. 3-4, pp. 261-273. https://doi.org/10.1504/IJEP.2006.011211