Real Time Flood Forecasting System Using Artificial Neural Networks

Authors(2) :-Avadhoot S. Idate, R. J. Deshmukh

Flood Forecasting is difficult task that faces fatal hazards due to fast rising stream flows from urban area. To avoid the future flood problems, to construct an on-line accurate model for forecasts flood levels during flood periods. The regions near Koyana and Krishna basins located in Maharashtra region is selected as study area. In this work, combining three ANNs to construct real time Flood Forecasting System. This paper suggests that the Flood Forecasting model can be valuable and very beneficial to flood control. We are considering the different location from where we can measure the outflow of the water so from which we can estimate the flood level of the location, which is affected by this location outflow directly.

Authors and Affiliations

Avadhoot S. Idate
Department of Computer Science and Technology, Department of Technology, Shivaji University, Kolhapur, India
R. J. Deshmukh
Department of Computer Science and Technology, Department of Technology, Shivaji University, Kolhapur, India

Artificial Neural Networks; Gamma Test; Flood Forecasting

  1. Fi-John Chang, Pin-An Chen, Ying-Ray Lu, Eric Huang, Kai-Yao Chang, "Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control",Journal of Hydrology 517 (2014) 836-846.
  2. D. Biondi, D.L. De Luca,"Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting",Journal of Hydrology 479 (2013) 51-63.
  3. Nien-Sheng Hsu, Chien-Lin Huang, Chih-Chiang Wei, "Multi-phase intelligent decision model for reservoir real-time floodcontrol during typhoons",Journal of Hydrology 522 (2015) 11-34.
  4. García-Pintado , David C. Mason , Sarah L. Dance , Hannah L. Cloke , Jeff C. Neal ,Jim FreerPaul D. Bates, Irene Kotsia et al, "Satellite-supported flood forecasting in river networks: A real case study", Journal of Hydrology 523 (2015) 706-724.
  5. Lu Chen, Yongchuan Zhan, Jianzhong Zhou, Vijay P. Singh ,ShenglianGuo ,Junhong Zhang,"Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting", Journal of Hydrology 521 (2015) 157-169.
  6. YoungminSeo , Sungwon Kim, Ozgur Kisi, Vijay P. Singh, "Daily water level forecasting using wavelet decompositionand artificial intelligence techniques", Journal of Hydrology 520 (2015) 224-243.
  7. Anil Kumar Lohani, N.K. Goel, K.K.S. Bh atia,"Improving real time flood forecasting using fuzzy inference system",Journal of Hydrology 509 (2014) 25-41.
  8. Seong Jin Noh, Oldr?ichRakovec, Albrecht H. Weerts,
  9. YasutoTachikawa, "On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models",Journal of Hydrology 519 (2014) 2707-2721.
  10. Yuan Li, DongryeolRyu, Andrew W. Western, Q.J. Wang, David E. Robertson,Wade T. Crow, "An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting", Journal of Hydrology 519 (2014) 2722-2736.
  11. Tsun-Hua Yang, Sheng-Chi Yang, Jui-Yi Ho, Gwo-Fong Lin , Gong-Do Hwang, Cheng-Shang Lee, "Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons",Journal of Hydrology 520 (2015) 367-378.
  12. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986." Learning representations by back propagating errors", Nature 323 (6088), 533-536.
  13. Agalbjorn, S., Koncar, N., Jones, A.J., "A note on the gamma test. Neuralcomput. Appl." 5 (1997), 131-133.
  14. Chang, F.J., Chang, K.Y., Chang, L.C., "Counterpropagation fuzzy-neural networkfor city flood control system", J. Hydrol. 358 (1)(2008), 24-34.
  15. Chang, L.C., Chen, P.A., Chang, F.J.," A reinforced two-step-ahead weight adjustment technique for on-line training of recurrent neural networks. IEEE Trans. Neural Network Learn. System", 23 (8)(2012), 1269-1278.
  16. Chang, F.J., Chen, P.A., Liu, C.W., Liao, V.H.C., Liao, C.M., "Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling", J. Hydrol. 499 (2013), 265-274.
  17. Elman, J.L.," Finding structure in time", Cognit. Sci. 14 (1990), 179-211.
  18. Assaad, M., BonÃl’, R., Cardot, H.,"Study of the behavior of a new boosting algorithm for recurrent neural networks", (2005)Lect. Notes Comput. Sci., 169-174.

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 738-742
Manuscript Number : CSEIT11724108
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Avadhoot S. Idate, R. J. Deshmukh, "Real Time Flood Forecasting System Using Artificial Neural Networks", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.738-742, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT11724108

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