1 |
Hitokoto, M., Sakuraba, M., and Sei, Y., 2016. Development of the Real-Time River Stage Predicition Method using Deep Learning. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 72(4), I_187-I_192. https://doi.org/10.2208/jscejhe.72.I_187
DOI
|
2 |
Nakatani. Y., Ishizaki. M., Nishida. S., 2017. Estimation of Water Quality Variation in a Tidal River by Applying Deep Learning Models. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 73(4), I_1141-I_1146. https://doi.org/10.2208/jscejhe.73.I_1141
DOI
|
3 |
Typhoon Research center, 2019. Typhoon Information. [Online] Available at: [Accessed May. 2019].
|
4 |
National Institute of Informatics (NII), 2019. Digital Typhoon. [Online] Available at: [Accessed June 2019].
|
5 |
Srivastava. N., Hinton. G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
|
6 |
Japan Meteorological Agency (JMA), 2019. Numerical Weather Prediction Activities Service System. [Online] Available at: [Accessed June. 2019].
|
7 |
Kim, Y-J., Tanaka, K., Nakashima, H., Nakakita, E., 2015. Debris Flow Prevention Countermeasures with Urban Inundation in a Multihazard-Environment. International Journal of Erosion Control Engineering, 9(2), 58-67. https://doi.org/10.13101/ijece.9.58
|
8 |
Kim, Y-J., Kim, T-W,, Yoon, J-S,, Kim, I-H., 2019. Study on Prediction of Similar Typhoons through Neural Network Optimization. Journal of Ocean Engineering and Technology, 33(5), 427-434. https://doi.org/10.26748/KSOE.2019.065
DOI
|
9 |
Sugiura. M., Tsujikura. H., Tanaka. K., 2015. Interpretation on the Temporal Change of Parameters in the Flood Prediction Model based on the Reserve Function Method. Japan Society of Civil Engineers, 71(4), I_307-I_312. https://doi.org/10.2208/jscejhe.71.I_307
|