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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:60368", author = "Muhammad Aqil and Ichiro Kita and Akira Yano and Nishiyama Soichi", title = "Decision Support System for Flood Crisis Management using Artificial Neural Network", abstract = "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.", keywords = "Decision Support System, Neural Network, Flood
Level", volume = "2", number = "3", pages = "875-7", }