All Issue

2020 Vol.7, Issue 2 Preview Page

Original Article


June 2020. pp. 114-125
Abstract


References
1 

Benzineb, K. and Remaoun, M. 2016. Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane's basin. Journal of Fundamental and Applied Sciences 8(3): 956-970.

10.4314/jfas.v8i3.17
2 

Chen, Z. and Ho, P.H. 2019. Global-connected network with generalized ReLU activation. Pattern Recognition 96: 106961.

10.1016/j.patcog.2019.07.006
3 

EGIS. 2010. Environmental Geographic Information Service. egis.me.go.kr.

4 

Farias, C.A., Santos, C.A., Lourenço, A.M. and Carneiro, T.C. 2013. Kohonen neural networks for rainfall-runoff modeling: case study of piancó river basin. Journal of Urban and Environmental Engineering 7(1): 176-182.

10.4090/juee.2013.v7n1.176182
5 

Ide, H. and Kurita, T. 2017. Improvement of learning for CNN with ReLU activation by sparse regularization. 2017 International Joint Conference on Neural Networks. Anchorage, AK. pp. 2684-2691.

10.1109/IJCNN.2017.7966185
6 

Kalteh, A.M. 2008. Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding Caspian. Journal of Environmental Science 6(1): 53-58.

7 

Kasiviswanathan, K.S, Sudheer, K.P. 2013. Quantification of the predictive uncertainty of artificial neural network based river flow forecast models. Stoch Environ Res Risk Assess 27(1): 137-146.

10.1007/s00477-012-0600-2
8 

Keras. 2019. www.keras.io.

9 

KMA. 2020. Korea Meteorological Administration. www.kma.go.kr.

10 

Lim, H., Kim, J., Kwon, D. and Han, Y. 2017. Comparison analysis of TensorFlow's optimizer based on MNIST's CNN model. Journal of Advanced Technology Research 2(1): 6-14.

11 

Maca, P., Pech, P., and Pavlasek, J. 2014. Comparing the selected transfer functions and local optimization methods for neural network flood runoff forecast. Mathematical Problems in Engineering 2014: 1-10.

10.1155/2014/782351
12 

Maier, H.A., Jain, G., Dandy, and Sudheer, K.P. 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software 25(8): 891-909.

10.1016/j.envsoft.2010.02.003
13 

Mishra, P.K. and Karmakar, S. 2019. Performance of optimum neural network in rainfall-runoff modeling over a river basin. International Journal of Environmental Science & Technology 16(3): 1289-1302.

10.1007/s13762-018-1726-7
14 

MLTM. 2012. Design Flood Estimation Techniques, Ministry of Land Transport and Maritime Affairs. (in Korean)

15 

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., and Veith, T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers 50(3): 885-900.

10.13031/2013.23153
16 

Nourani, V., Komasi, M., and Alami, M.T. 2012. Hybrid wavelet-genetic programming approach to optimize ANN modeling of rainfall-runoff process. Journal of Hydrologic Engineering 17(6): 724-741.

10.1061/(ASCE)HE.1943-5584.0000506
17 

Othman, F. and Naseri, M. 2011. Reservoir inflow forecasting using artificial neural network. International Journal of the Physical Sciences 6(3): 434-440.

18 

Patel, B. and Joshi, G.S. 2017. Civil modeling of rainfall-runoff correlations using artificial neural network - A case study of Dharoi watershed of a Sabarmati river basin, India. Ajay Engineering Journal 3(2): 78-87. (online).

10.28991/cej-2017-00000074
19 

Python 3.7. 2018, www.python.org. Released 27 June 2018.

20 

Rallison, R.E. 1980. Origin and evolution of the SCS runoff equation. In Symposium on Watershed Management 1980, ASCE. pp. 912-924.

21 

Shoaib, M., Shamseldin, A.Y., Melville, B.W. and Khan, M.M. 2016. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. Journal of Hydrology 535: 211-225.

10.1016/j.jhydrol.2016.01.076
22 

Singh, P.V., Akhilesh, K., Rawat, J.S., and Devendra, K. 2013. Artificial neural networks based daily rainfall-runoff model for an agricultural hilly watershed. International Journal of Engineering, Management & Sciences 4(2): 108-112.

23 

Tensorflow. 2019. www.tensorflow.org.

24 

WAMIS. 2003. Water Resource Management Information System. www.wamis.go.kr.

25 

Wu, C.L. and Chau, K.W. 2011. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology 399(3-4): 394-409.

10.1016/j.jhydrol.2011.01.017
26 

Zhang, B. and Govindaraju, R.S. 2000. Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resources Research 36(3): 753-762.

10.1029/1999WR900264
Information
  • Publisher :Korean Society of Ecology and Infrastructure Engineering
  • Publisher(Ko) :응용생태공학회
  • Journal Title :Ecology and Resilient Infrastructure
  • Journal Title(Ko) :응용생태공학회 논문집
  • Volume : 7
  • No :2
  • Pages :114-125
  • Received Date :2020. 05. 12
  • Revised Date :2020. 06. 07
  • Accepted Date : 2020. 06. 10