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Journal of the Korean Society of Civil Engineers

Journal of the Korean Society of Civil Engineers

ISO Journal TitleKSCE J. Civ. Environ. Eng. Res.
  • Open Access, Bi-monthly

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  3. 2020-06 (v.40 n.3)
  4. 10.12652/Ksce.2020.40.3.0273
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1 
Cultural Heritage Administration (2020). Flood history, Available at: http://royaltombs.cha.go.kr (Accessed: January 2, 2020).
2 
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.DOI
3 
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.DOI
4 
Korea Meteorological Administration (KMA) (2020). Meteorological database, Observation Data, Available at: https://data. kma.go.kr (Accessed: December 1, 2019).
5 
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.DOI
6 
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).
7 
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.DOI
8 
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.DOI
9 
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.DOI
10 
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.
11 
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.
12 
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.DOI
13 
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.DOI
14 
Seoul Metropolitan City (2015). Comprehensive plan for storm and flood damage reduction, Korea, Vol. 1, Chapter 3, pp. 374-375.
15 
Seoul Metropolitan Government (2020). Flood criterion data, Available at: http://safecity.seoul.go.kr (Accessed: January 5, 2020)
16 
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.DOI
17 
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.DOI