References
- Araghinejad, S. (2013). Data-Driven Modeling: Using MATLAB in Water Resources and Environmental Engineering. Water Science and Technology Library, Springer.
- Chang, C.C. and Lin, C.J. (2001). LIBSVM: A Library for Support Vector Machines. Department of Computer Science, National Taiwan University, Taipei, Taiwan.
- Chau, K.W. and Wu, C.L. (2010). Hydrological Predictions Using Data-Driven Models Coupled with Data Preprocessing Techniques. Lambert Academic Publishing.
- Choi, H.G., Han, K.Y., Roh, H.S. and Park, S.J. (2013). Comparison of databased real-time flood forecasting model. Journal of the Korean Society of Civil Engineers, Vol. 33, No.5, pp. 1809-1827. https://doi.org/10.12652/Ksce.2013.33.5.1809
- Kim, H.I., Keum, H.J. and Han, K.Y. (2018). Estimation of inundation area by linking of rainfall-duration-flooding quantity relationship curve with self-organizing map. Journal of the Korean Society of Civil Engineers, Vol. 38, No.6, pp. 839-850. https://doi.org/10.12652/KSCE.2018.38.6.0839
- Kim, J.H., Lee, S.W. and Cha, S.M. (2016). Environmental Statistics & Data Analysis. Hannarae.
- Murphy, K.P. (2012). Machine Learning : A Probabilistic Perspective. Massachusetts Institute of Technology.
- Noh, Y.J. (2016). A comparison study on statistical modeling methods. Journal of the Korea Academia-Industiral cooperation Society. Vol. 17, No.5, pp. 645-652. https://doi.org/10.5762/KAIS.2016.17.5.645
- Park, J.H., Lee, J.J. and Lee, S.H. (2018). Statistical significance test of polynomial regression equation for Huff's quartile method of design rainfall. J. Korea Water Resour. Assoc., Vol. 51, No.3, pp. 263-272. https://doi.org/10.3741/JKWRA.2018.51.3.263
- 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.
- Robson, A.J., Jones, T.K., Reed, D.W. and Bayliss, A.C. (1997). A study of national trend and variation in UK floods. International J. Climatology, Vol. 18, pp.165-182. https://doi.org/10.1002/(SICI)1097-0088(199802)18:2<165::AID-JOC230>3.0.CO;2-#
- Sun, A.Y., Wang, D. and Xu, X. (2014). Monthly streamflow forecasting using gaussian process regression. Journal of Hydrology. Vol. 511, pp. 72-82. https://doi.org/10.1016/j.jhydrol.2014.01.023
- Vapnic, V.N. (1998). Statistical Learning Theory. John Wiley & Sons, New York.
- Yu, P.S., Chen, S.T. and Chang, I.F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328, 704-716. https://doi.org/10.1016/j.jhydrol.2006.01.021