Decision Support System for Flood Crisis Management using Artificial Neural Network

This paper presents an alternate approach that uses artificial neural network to simulate the flood level dynamics in a river basin. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach and evolving graphical feature and can be adopted for any similar situation to predict the flood level. The main data processing includes the gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood level data, to train/test the model using various inputs and to visualize results. The program code consists of a set of files, which can as well be modified to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The running results indicate that the decision support system applied to the flood level seems to have reached encouraging results for the river basin under examination. The comparison of the model predictions with the observed data was satisfactory, where the model is able to forecast the flood level up to 5 hours in advance with reasonable prediction accuracy. Finally, this program may also serve as a tool for real-time flood monitoring and process control.

Authors:

• Muhammad Aqil
• Ichiro Kita
• Akira Yano
• Nishiyama Soichi

Keywords:

• Decision Support System
• Neural Network
• Flood Level

References:

[1] Q. Duan, S. Sorooshian, and V.K. Gupta, "Effective and efficient global
optimization for conceptual rainfall runoff models," Water Resour. Res.,
vol. 28, pp. 1015-1031, 1992.
[2] A. W. Minns, and M.J. Hall, "Artificial neural networks as rainfallrunoff
models," Hydrol. Sci. J., vol. 41, pp. 399-418, 1996.
[3] S. Openshaw, and C. Openshaw, "Artificial Intelligence in Geography,",
Chichester : John Wiley & Sons Ltd, 1997.
[4] T. Y. Pan, and R. Y. Wang, "State space neural networks for short term
rainfall-runoff forecasting," J. Hydrol., vol. 297, pp. 34-50, 2004.
[5] C. E. Imrie, S. Durucan, and A. Korre, "River flow prediction using
artificial neural networks: generalisation beyond the calibration range,"
J. Hydrol., vol. 233, pp. 138-153, 2000.
[6] T. S. Hu, K. C. Lam, and S. T. Ng, S.T, "River flow time series
prediction with a range-dependent neural network," Hydrol. Sci. J., vol.
46, pp. 729-745, 2001.
[7] D. N. Kumar, K. S. Raju, T. Sathish, T, "River Flow Forecasting using
Recurrent Neural Networks," Water Resour. Mgmt., vol.18, pp. 143-
161, 2004.
[8] A. Jain, K. P. Sudheer, and S. Srinivasulu, "Identification of physical
processes inherent in artificial neural network rainfall-runoff models,"
Hydrol. Process. vol. 118, vol. 571-581, 2004.
[9] K. Hsu, H. V. Gupta, and S. Sorooshian, "Artificial neural network
modelling of the rainfall-runoff process," Water Resour. Res., vol. 31,
pp. 2517-2530, 1995.
[10] R. Kuligowski, and A. P. Barros, "Localized precipitation forecasts from
a numerical weather prediction model using artificial neural networks,"
Wea. Forecast., vol. 13, pp. 1195-1205, 1998.
[11] K. C. Luk, J. E. Ball, and A. Sharma, "An application of artificial neural
networks for rainfall forecasting," Math. Computer Mod., vol. 33, pp.
683-693, 2001.
[12] M. C. P. Rami'rez, H. F. C. Velho, and N. J. Ferreira, N.J, "Artificial
neural network technique for rainfall forecasting applied to the Sa˜o
Paulo region," J. Hydrol., vol. 301, pp. 146-162, 2005.
[13] R. S. Ranjithan, D. E. Eheart, and J. H. Garrett, "Application of neural
network in groundwater remediation under conditions of uncertainty,"
in New Uncertainty Concepts in Hydrology and Water Resources-,
edited by Z. W. Kundzewicz, 133-144, Cambridge University Press,
New York, 1995.
[14] C. C. Yang, S. O. Prasher, R. Lacroix, S. Sreekanth, N. K. Patni, and L.
Masse, L, "Artificial neural network model for subsurface-drained
farmland," J. Irrig. Drain. Eng. vol. 123, pp. 285-292, 1997.
[15] P. Coulibaly, F. Anctil, R. Aravena, and B. Bobe'e, "Artificial neural
network modeling of water table depth fluctuations," Water Resour. Res.
vol. 37, pp. 885-896, 2001.
[16] I. N. Daliakopoulos, P. Coulibalya, and I. K. Tsanis, "Groundwater level
forecasting using artificial neural networks," J. Hydrol. vol. 309,
pp.229-240, 2005.
[17] C. W. Dawson, and R. L. Wilby, "A comparison of artificial neural
networks used for rainfall-runoff modeling," Hydrol. and Earth Syst.
Sci., vol. 3, pp. 529-540, 2000.
[18] A. S. Tokar, and P. A. Johnson, "Rainfall-runoff modelling using
artificial neural Networks," J. Hydrol. Eng. Vol. 4, pp. 232-239, 1999.
[19] S. Riad, J. Mania, L. Bouchaou, and Y. Najjar, "Predicting catchment
flow in a semi-arid region via an artificial neural network technique,"
Hydrol. Process. vol. 18, pp. 2387-2393, 2004.
[20] P.C. Nayak, K.P. Sudheer, D.M. Rangan, and K.S. Ramasastri, "Shortterm
flood forecasting with a neurofuzzy model," Water Resour. Res.
vol. 41, pp. 2517-2530, 2005.
[21] ASCE Task Committee, "Artificial neural networks in hydrology," J.
Hydrol. Eng. vol. 5, pp. 115-123, 2000.
[22] H. Maier, and G. Dandy, "Neural networks for the predictions and
forecasting of water resources variables: review of modeling issues and
applications," Environ. Modelling and Soft., vol. 15, pp. 101-124, 2000.
[23] H. Demuth, and M. Beale, "Neural Network Toolbox for Use with
MATLAB, Users Guide, Version 3,". The MathWorks, Inc.,
Massachusetts, 1998.
[24] K. P. Sudheer, A. K. Gosain, and K. S. Ramasastri, "A data-driven
algorithm for constructing artificial neural network rainfall-runoff
models," Hydrol. Process. vol. 16, 1325-1330, 2002.