A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers - Advancing Artificial Intelligence Through Biological Process Applications - page 375

communities: concepts, methods and problems,
160 (3), 249-264.

Milanese, M., & Novara, C. (2004). Nonlinear Set
Membership prediction of river flow. Systems &
Control Letters, 53 (1), 31-39.

Guymer, I. (1998). Longitudinal dispersion in
sinuous channel with changes in shape. Journal
of Hydraulic Engineering, 124 (1), 33-40.

Negnevitsky, M. (2001). Artificial intelligence: A
guide to intelligent systems . Boston, MA, USA:
Addison-Wesley Longman Publishing Co., Inc.

Imrie, C. E., Durucan, S., & Korre, A. (2000).
River flow prediction using artificial neural
networks: generalisation beyond the calibration
range. Journal of Hydrology, 233 (1-4), 138-153.

Príncipe, J. C., Euliano, N. R., & Lefebvre, W. C.
(1999). Neural and adaptive systems: fundamen-
tals through simulations . New York: Wiley.

Jobson, H. E. (2001). Predicting river travel time
from hydraulic characteristics. Journal of Hy-
draulic Engineering, 127 (11), 911-918.

Riad, S., Mania, J., Bouchaou, L., & Najjar, Y.
(2004). Rainfall-runoff model usingan artificial
neural network approach. Mathematical and
Computer Modelling, 40 (7-8), 839-846.

Juha, V., & Esa, A. (2000). Clustering of the self-
organizing map. IEEE Transactions on Neural
Networks, 11 (3), 586-600.

Saad, E. W., & Wunsch Ii, D. C. (2007). Neural
network explanation using inversion. Neural
Networks, 20 (1), 78-93.

Kerh, T., & Lee, C. S. (2006). Neural networks
forecasting of flood discharge at an unmeasured
station using river upstream information. Advanc-
es in Engineering Software, 37 (8), 533-543.

Saito, K., & Nakano, R. (2002). Extracting regres-
sion rules from neural networks. Neural Networks,
15 (10), 1279-1288.

Kingston, G. B., Maier, H. R., & Lambert, M.
F. (2006). A probabilistic method for assisting
knowledge extraction from artificial neural
networks used for hydrological prediction. Ap-
plication of Natural Computing Methods to
Water Resources and Environmental Modelling,
44 (5-6), 499-512.

Seo, S., & Obermayer, K. (2004). Self-organizing
maps and clustering methods for matrix data.
New Developments in Self-Organizing Systems,
17 (8-9), 1211-1229.

Sivakumar, B., Jayawardena, A. W., & Fernando,
T. M. K. G. (2002). River flow forecasting: use of
phase-space reconstruction and artificial neural
networks approaches. Journal of Hydrology,
265 (1-4), 225-245.

Maier, H. R., & Dandy, G. C. (1998). Understand-
ing the behaviour and optimising the performance
of back-propagation neural networks: an empirical
study. Environmental Modelling and Software,
13 (2), 179-191.

Sudheer, K. P. (2005). Knowledge extraction from
trained neural network river flow models. Journal
of Hydrologic Engineering, 10 (4), 264-269.

Maier, H. R., & Dandy, G. C. (2000). Neural net-
works for the prediction and forecasting of water
resources variables: A review of modelling issues
and applications. Environmental Modelling and
Software, 15 (1), 101-124.

Waldon, M. G. (2004). Estimation of average
stream velocity. Journal of Hydraulic Engineer-
ing, 130 (11), 1119-1122.

Zha, H., Ding, C., Gu, M., He, X., & Simon, H.
(2002). Spectral relaxation for k-means cluster-
ing. Advances in Neural Information Processing
Systems, 14 , 1057-1064.