Cultural Heritage Administration (2020). Flood history, Available at: http://royaltombs.cha.go.kr (Accessed: January 2, 2020).
Gardner, K. K. and Vogel, R. M. (2005). "Predicting ground water nitrate concentration from land use." Ground Water, Vol. 43, No. 3, pp. 343-352.10.1111/j.1745-6584.2005.0031.x15882326
Hu, C., Wu, Q., Li, H., Jian, S., Li, N. and Lou, Z. (2018). "Deep learning with a long short-term memory networks approach for rainfall-runoff simulation." Water, Vol. 10, No. 11, 1543.10.3390/w10111543
Korea Meteorological Administration (KMA) (2020). Meteorological database, Observation Data, Available at: https://data. kma.go.kr (Accessed: December 1, 2019).
Le, X. H., Ho, H. V., Lee, G. H. and Jung, S. H. (2019). "Application of long short-term memory (LSTM) neural network for flood forecasting." Water, Vol. 11, No. 7, 1387, DOI: 10.3390/w11071387.10.3390/w11071387
Moore, M. R. (2011). Development of a high-resolution 1D/2D coupled flood simulation of Charles City, Iowa, Master's Thesis, University of Iowa, United States of America (USA).
Mosavi, A., Ozturk, P. and Chaw K. K. (2018). "Flood prediction using machine learning models: Literature review." Water, Vol. 10, No. 11, 1536, DOI: 10.3390/w10111536.10.3390/w10111536
Nandi, A., Mandal, A., Wilson, M. and Smith, D. (2016). "Flood hazard mapping in Jamaica using principal component analysis and logistic regression." Environment Earth Science, Vol. 75, No. 465, DOI: 10.1007/s12665-016-5323-0.10.1007/s12665-016-5323-0
Ozdemir, A. (2011). "Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)." Journal of Hydrology, Vol. 405, No. 1-2, pp. 123-136.10.1016/j.jhydrol.2011.05.015
Park, J. H., Kim, S. H. and Bae, D. H. (2019). "Evaluating appropriateness of the design methodology for urban sewer system." Journal of Korea Water Resource Association, KWRA, Vol. 52, No. 6, pp. 411-420.
Pradhan, B. (2009). "Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing." Journal of Spatial Hydrology, Vol. 9, No. 2, pp. 1-18.
Rai, P. K., Chahar, B. R. and Dhanya, C. T. (2017). "GIS-based SWMM model for simulating the catchment response to flood evetns." Hydrology Research, Vol. 48, No. 2, pp. 384-394.10.2166/nh.2016.260
Risi, R. D., Jalayer, F. and Paola, F. D. (2015). "Meso-scale hazard zoning of potentially flood prone areas." Journal of Hydrology, Vol. 527, pp. 316-325.10.1016/j.jhydrol.2015.04.070
Seoul Metropolitan City (2015). Comprehensive plan for storm and flood damage reduction, Korea, Vol. 1, Chapter 3, pp. 374-375.
Seoul Metropolitan Government (2020). Flood criterion data, Available at: http://safecity.seoul.go.kr (Accessed: January 5, 2020)
Shen, C. (2018). "A transdisciplinary review of deep learning research and its relevance for water resources scientists." Water Resources Research, Vol. 54, No. 11, pp. 8558-8593, DOI: 10.1029/2018WR022643.10.1029/2018WR022643
Son, A. L., Kim, B. H. and Han, K. Y. (2015). "A study on prediction of inundation area considering road network in urban area." Journal of the Korean Society of Civil Engineers, KSCE, Vol. 35, No. 2, pp. 307-318.10.12652/Ksce.2015.35.2.0307
- Publisher :Korean Society of Civil Engineers
- Publisher(Ko) :대한토목학회
- Journal Title :JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS
- Journal Title(Ko) :대한토목학회 논문집
- Volume : 40
- No :3
- Pages :273-283
- Received Date :2020. 02. 12
- Revised Date :2020. 02. 26
- Accepted Date : 2020. 04. 06