1 |
H. L. Cloke and F. Pappenberger, "Ensemble flood forecasting: A review," Journal of Hydrology, vol. 375, 2009, pp. 613-626.
DOI
|
2 |
L. H. Feng and J. Lu, "The practical research on flood forecasting based on artificial neural networks," Expert Systems with Applications, vol. 37, 2010, pp. 2974-2977.
DOI
|
3 |
D. Demeritt, H. Cloke, F. Pappenberger, J. Thielen, J. Bartholmes, and M.-H. Ramos, "Ensemble predictions and perceptions of risks, uncertainty, and error in flood forecasting," Environmental Hazards, vol. 7, no. 2, 2007, pp.115-127.
DOI
|
4 |
A. de Roo, B. Gouweleeuw, J. Thielen, J. Bartholmes, P. Bongioannini-Cerlini, E. Todini, P. D. Bates, M. Horritt, N. Hunter, K. Beven, F. Pappenberger, E. Heise, G. Rivin, M. Hils, A. Hollingsworth, B. Holst, J. Kwadijk, P. Reggiani, M. van Dijk, K. Sattler, and E. Sprokkereef, "Development of a European flood forecasting system," International Journal of River Basin Management, vol. 1, 2003, pp. 49-59.
DOI
|
5 |
F. Altiparmak, B. Dengiz, and A. E. Smith, "A general neural network model for estimating telecommunications network reliability," IEEE Trans. Reliability, vol. 58, issue. 1, 2009, pp. 2-9.
DOI
|
6 |
I. Kaaastra and M. Boyd, "Designing a neural network for forecasting financial and economic time series," Neurocomputing, vol. 10, issue. 3, 1996, pp. 215-236.
DOI
|
7 |
Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, 2015, pp. 436-444.
DOI
|
8 |
A. Atiya and S. Shaheen, "A comparison between neural-network forecasting techniques-case study: river flow forecasting," IEEE Trans. Neural Networks, vol. 10, no. 2, 1999, pp. 402-409.
DOI
|
9 |
C. W. Dawson, R. J. Abrahart, A. Y. Shamseldin, and R. L. Wilby, "Flood estimation at ungauged sites using artificial neural networks," Journal of Hydrology, vol. 319, 2006, pp. 391-409.
DOI
|
10 |
Y. Wei, W. Xu, Y. Fan, and H.-T. Tasi, "Artificial neural network based predictive method for flood disaster," Computers & Industrial Engineering, vol. 42, 2002, pp. 383-390.
DOI
|
11 |
M. P. Rajurkar, U. C. Kothyari, and U. C. Chaube, "Modelling of the daily rainfall-runoff relationship with artificial neural network," Journal of Hydrology, vol. 285, 2004, pp. 96-113.
DOI
|
12 |
S. Raid, J. Mania, L. Bouchaou, and Y. Najjar, "Rainfall-runoff model using an artificial neural network approach," Mathematical and Computer Modelling, vol. 40, 2004, pp. 839-846.
DOI
|
13 |
S. Raid, J. Mania, L. Bouchaou, and Y. Najjar, "Predicting catchment flow in a semi-arid region via an artificial neural network," Hydrological Processes, vol. 18, 2004, pp. 2387-2393.
DOI
|
14 |
F. J. Chang, P. A. Chen, Y. R. Lu, E. Huang, and K. Y. Chang, "Real-time multi-step-ahead water level forecasting by recurrent neural neyworks for urban flood control," Journal of Hydrology, vol. 517, 2014, pp. 836-846.
DOI
|
15 |
L. H. C. Chua and T. S. W. Wong, "Improving event-based rainfall-runoff modeling using a combined artificial neural network-kinematic wave approach," Journal of Hydrology, vol. 390, 2010, pp. 92-107.
DOI
|
16 |
D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, Cambridge, MA, 1986.
|
17 |
Q. K. Tran and S. K. Song, "Water level forecasting based on deep learning: a use case of Trinity River-Texas-The United Sattes," Journal of KIISE, vol. 44, 2017, pp. 607-612.
DOI
|
18 |
S. H. Oh, "Improving the error back-propagation algorithm with a modified error function," IEEE Trans. Neural Networks, vol. 8, 1997, pp. 799-803.
DOI
|
19 |
J. B. Hampshire II and A. H. Waibel, "A novel objective function for improved phoneme recognition using time-delay neural networks," IEEE Trans. Neural Networks, vol. 1, 1990, pp. 216-228.
DOI
|
20 |
L. Bruzzone and S. B. Serpico, "Classification of imbalanced remote-sensing data by neural networks," Pattern Recognition Letters, vol. 18, 1997, pp. 1323-1328.
DOI
|
21 |
R. K. Cheing, I. Lustig, and A. L. Kornhauser, "Relative effectiveness of training set patterns for backpropagation," Proc. IJCNN, Washington, D.C., Jan 15-19, 1990, vol. I, pp. 673-678.
|
22 |
A. van Ooyen and B. Nienhuis, "Improving the convergence of the backpropagation algorithm," Neural Networks, vol. 5, 1992, pp. 465-471.
DOI
|
23 |
S. H. Oh and H. Wakuya, "Hydrological modeling of water level near "Hahoe Village" based on multi-layer perceptron," Int. Journal of Contents, vol. 12, no. 1, 2016, pp. 49-53.
DOI
|
24 |
C. Phua, D. Alahakoon, and V. Lee, "Minority report in fraud detection: classification of skewed data," ACM SIGKDD Explorations Newsletter, vol. 6, issue. 1, 2004, pp. 50-59.
DOI
|
25 |
S. H. Oh, "Error back-propagation algorithm for classification of imbalanced data," Neurocomputing, vol. 74, 2011, pp. 1058-1061.
DOI
|
26 |
C. W. Dawson and R. L. Wilby, "Hydrological modeling using artificial neural networks," Progress in Physical Geography, vol. 25, 2001, pp. 80-108.
DOI
|
27 |
K. Hornik, M. Stinchcombe, and H. White, "Multilayer Feed-forward Networks are Universal Approximators," Neural Networks, vol. 2, 1989, pp. 359-366.
DOI
|
28 |
K. Hornik, "Approximation Capabilities of Multilayer Feedforward Networks," Neural Networks, vol. 4, 1991, pp. 251-257.
DOI
|
29 |
S. Suzuki, "Constructive Function Approximation by Three-Layer Artificial Neural Networks," Neural Networks, vol. 11, 1998, pp. 1049-1058.
DOI
|
30 |
C. M. Bishop, Pattern Recognition and Machine Learning, Springer Science+Business Media LLC, 2006.
|