1 |
Kao, I.F., Zhou, Y.L., Chang, L.C., and Chang, F.J. (2020). "Exploring a long short-term memory based encoder-decoder framework for multi-step-ahead flood forecasting." Journal of Hydrology, Vol. 583, 124631.
DOI
|
2 |
Kim, J.H., Jun, S.M., Hwang, S.H., Kim, H.K., Heo, J.M., and Kang M.S. (2021). "Impact of activation functions on flood forecasting model based on artificial neural networks" Journal of the Korean Society of Agricultural Engineers, Vol. 63, No. 1, pp. 11-25.
DOI
|
3 |
Eom, J.I., and Jung, K.S. (2019). "Estimation of Hourly Dam inflow using time series data." Journal of Korean Society of Hazard Mitiggation, Vol. 19, No. 2, pp. 163-168.
|
4 |
Fuente, A., Meruane, V., and Meruane, C. (2019). "Hydrological early warning system based on a deep learning runoff model coupled with a meteorological forecast." Water, Vol. 11, No. 9, pp. 1808.
DOI
|
5 |
Hsu, K.L., Gupta, H.V., and Sorooshian, S. (1995). "Artificial neural network modeling of the rainfall-runoff process." Water Resources Research, Vol. 31, No. 10, pp. 2517-2530.
DOI
|
6 |
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: 10.3390/w10111543
DOI
|
7 |
Jain, A., and Kumar, A.M. (2007). "Hybrid neural network models for hydrologic time series forecasting." Applied Soft Computing, Vol. 7, No. 2, pp. 585-592.
DOI
|
8 |
Jeong, D.M., and Bae, D.H, (2004), "Monthly Dam inflow forecasts by using weather forecasting information." Journal of Korea Water Resources Association, Vol. 37, No. 6, pp. 449-460.
DOI
|
9 |
Kim, J.H. (1993). A study on hydrologic forecasting of streamflows based on artificial neural network, Ph.D, dissertation. Inha University.
|
10 |
Cui, Z., Zhou, Y., Guo, S., Wang, J., Ba, H., and He, S. (2021). "A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting." Hydrology Research. doi: 10.2166/nh.2021.016
DOI
|
11 |
Sit, M., Demiray, B.Z., Xiang, Z., Ewing, G.J., Sermet, Y., and Demir, I. (2020). "A comprehensive review of deep learning applications in hydrology and water resources." Water Science and Technology, Vol. 82, No. 12, pp. 2635-2670.
DOI
|
12 |
Kingma, D.P., and Ba, J. (2015). "Adam : A method for stochastic optimization." 3rd International Conference on Learning Representations 2015, San Diego, CA, U.S., arXiv:1412.6980.
|
13 |
Kumar, S., Tiwari, M.K., Chatterjee, C., and Mishra, A. (2015). "Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method." Water Resources Management, Vol. 29, No. 13, pp. 4863-4883.
DOI
|
14 |
Lee, J.Y., Kim, H.I., and Han, K.Y. (2020). "Linkage of hydrological model and machine learning for real-time prediction of river flood." Journal of Korean Society of Civil Engineers, Vol. 40, No. 3, pp. 303-314.
DOI
|
15 |
Noori, N., and Kalin, L. (2016) "Coupling SWAT and ANN models for enhanced daily streamflow prediction." Journal of Hydrology, Vol. 533, pp. 141-151.
DOI
|
16 |
Sim, S.B., and Kim, M.S. (1998). "Flood inflow forecasting on multipurpose reservoir by neural network." Journal of Korea Water Resources Association, Vol. 31, No. 1, pp. 45-57.
|
17 |
Thomas, V., and Lopez, R. (2015) Global increase in climate-related disasters. ADB Economics Working Paper Series, No. 466, Asian Development Bank, Mandaluyong, Philippines.
|
18 |
Xiang, Z., Yan, J., and Demir, I. (2020). "A rainfall-runoff model with LSTM-based sequence-to-sequence learning." Water Resources Research, Vol. 56, No. 1, e2019WR025326.
|
19 |
Mosavi, A., Ozturk, P., and Chau, K.W. (2018) "Flood prediction using machine learning models: Literature review." Water, Vol. 10, No. 11, 1536.
DOI
|
20 |
Yoo, C.S., Hwang, J.H., and Kim, J.H. (2012). "Use of the extended Kalman Filter for the real-time quality improvement of runoff data: 1. Algorithm construction and application to one station." Journal of Korea Water Resources Association, Vol. 45, No. 7, pp. 697-711.
DOI
|
21 |
Taieb, S.B., Sorjamaa, A., and Bontempi, G. (2010). "Multiple-output modeling for multi-step-ahead time series forecasting." Neurocomputing, Vol. 73, No. 10-12, pp. 1950-1957.
DOI
|
22 |
Yaseen, Z.M., El-Shafie, A., Jaafar, O., Afan, H.A., and Sayl, K.N. (2015). "Artificial intelligence based models for stream-flow forecasting: 2000-2015." Journal of Hydrology, Vol. 530, pp. 829-844.
DOI
|
23 |
Imrie, C.E., Durucan, S., and Korre, A. (2000). "River flow prediction using artificial neural networks: Generalisation beyond the calibration range." Journal of HYdrology, Vol. 233, No. 1-4, pp. 138-153.
DOI
|
24 |
Chen, S.M., Wang, Y.M., and Tsou, I. (2013). "Using artificial neural network approach for modelling rainfall-runoff due to typhoon." Journal of Earth System Science, Vol. 122, No. 2, pp. 399-405.
DOI
|
25 |
Kim, S.W. (2000). "A study on the forecasting of daily streamflow using the multilayer neural networks model." Journal of Korea Water Resources Association, Vol. 33, No. 5, pp. 537-550.
|
26 |
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M. (2018). "Rainfall-runoff modelling using long short-term memory (LSTM) networks." Hydrology and Earth System Sciences, Vol. 22, No. 11, pp. 6005-6022.
DOI
|
27 |
Mok, J.Y., Choi, J.H., and Moon, Y.I. (2020). "Prediction of multipurpose dam inflow using deep learning." Journal of Korea Water Resources Association, Vol. 53, No. 2, pp. 97-105.
DOI
|
28 |
Park, M.K., Yoon, Y.S., Lee, H.H., and Kim, J.H. (2018). "Application of recurrent neural network for inflow prediction into multi-purpose dam basin." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1217-1227.
|
29 |
Tabari, H. (2020). "Climate change impact on flood and extreme precipitation increases with water availability." Scientific Reports, Vol. 10, No. 1, pp. 1-10.
DOI
|
30 |
Toth, E., and Brath, A. (2007). "Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling." Water Resources Research, Vol. 43, No. 11.
|
31 |
Dawson, C.W., and Wilby, R. (1998). "An artificial neural network approach to rainfall-runoff modelling." Hydrological Sciences Journal, Vol. 43, No. 1, pp.47-66.
DOI
|
32 |
Chang, F.J., Chiang, Y.M., and Chang, L.C. (2007). "Multi-step-ahead neural networks for flood forecasting." Hydrological Sciences Journal, Vol. 52, No. 1, pp. 114-130.
DOI
|
33 |
Chollet, F. (2017). Deep learning with Python. Manning, Shelter Island, NY, U.S.
|
34 |
Cook, B.I., Mankin, J.S., Marvel, K., Williams, A.P., Smerdon, J.E., and Anchukaitis, K.J. (2020). "Twenty-first century drought projections in the CMIP6 forcing scenarios." Earth's Future, Vol. 8, No. 6, e2019EF001461.
|
35 |
Devia, G.K., Ganasri, B.P., and Dwarakish, G.S. (2015). "A review on hydrological models." Aquatic Procedia, Vol. 4, pp. 1001-1007.
DOI
|
36 |
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press, Cambridge, MA, U.S.
|
37 |
Kang, M.S., and Park, S.W. (2003). "Short-term flood forecasting using artificial neural networks." Journal of the Korean Society of Agricultural Engineers, Vol. 45, No. 2, pp. 45-57.
|