شریفی م. و صالحی سده ر. ١٣٨٤. کاربرد شبکههای عصبی در پیش بینی جریان رودخانه در حوزه معرف کارده. کمیته تحقیقات شرکت سهامی آب منطقه ای خراسان. دفتر فنی و پژوهشهای کاربردی شرکت مدیریت منابع آب ایران.
Abrahart, R. J. , See, L. , Kneal, P. E. , 2001. Investigating the role of saliency analysis with neural network rainfall–runoff model. Computers and Geosciences 27,921-928.
Alp, M. , C. g. . zog. lu, H. K. , 2004. Modelling of rainfall. runoff relationship by different artificial intelligence methods. ITU Journal 3 (1), 80. 88 (In Turkish).
Bhattacharya, B. , and Solomatine, D. P. , Neural networks and M5 model trees in modeling water level–discharge relationship, Neurocomputing, 2005, vol. 63,pp. 381–396.
Cigizoglu, H. K. , H. K. , 2002. Suspended sediment estimation and forecasting using artificial neural networks. Turkish Journal of Engineering and Environmental Sciences 26 (2002), 16. 26
Cigizoglu, H. K. and Alp, M. , 2008. Generalized Regression Neural Network in Modeling River Sediment Yield. Journal of Advances in Engineering Software, 37: 63-67.
Erkan Turan, M. , Ali Yurdusev, M. 2009. River flow estimation from upstream flow records by artificial intelligence methods. Journal of hydrology. 369, 71-77.
Furundzic, D. , 1998. Application example of neural networks for time series analysis: rainfall–runoff modeling. Signal Processing 64, 383–396.
Lippmann, R. P. , 1987. An introduction to computing with neural nets. IEEE ASSP Mag. , 4–22.
Minns, A. W. , Hall, M. J. , 1996. Artificial neural networks as rainfall–runoff models. Hydrological Sciences Journal 41 (3), 399–417.
Quinlan, J. R. , Learning with continuous classes. In: Proc. AI'92 (Fifth Australian Joint Conf. on Arti_cial Intelligence)(ed. by A. Adams & L. Sterling), 343{348. World Scienti_c, Singapore. 1992.
Sajikumar, N. , Thandaveswara, B. S. , 1999. A non-linear rainfall–runoff model using an artificial neural network. Journal of Hydrology 216, 32–55.
See, L. , Corne, S. , Dougherty, M. , Openshaw, S. , 1997. Some initial experiments with neural network models of flood forecasting on the River Ouse. In: Proceedings of the 2nd International Conference on GeoComputation.
Shamseldin, A. Y. , 1997. Application of a neural network technique to rainfall–runoff modeling. Journal of Hydrology 199, 272–294.
Solomatine, D. P. and Avila Torres, L. A. , Neural network approximation of a hydrodynamic model in optimizing reservoir operation, In: Proceedings of the second international conference on hydroinformatics, Zurich. pp. 201-206, 1996.
Stravs, L. and Brilly, M. , Development of a low flow forecasting model using the M5 machine learning method, Hydrological Sciences, 2007, vol. 52, no. 3, pp. 466–477.
Tang, Z. , Fishwick, P. A. , 1993. Feedforward neural nets as models for time series forecasting. ORSA J. Comput. 5 (4), 374–385.
Wei Chih_Chiang and Hsu Nien_Sheng, Optimal tree based release rules for real_time flood control operations on a multipurpose multireservoir system, J. Hydrology, 2009, vol. 365, pp. 213–224.
Witten, I. H. and Frank, E. , Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann: San Francisco. 2005.
Wong, F. S. , 1991. Time series forecasting using backpropagation neural networks. Neurocomputing 2, 147–159.
Toker, A. S. and Markus, M. 2000. Precipitation-Runoff Modeling using Artificial Neural Network and ConceptualModels, Journal of Hydrologic Engineering, 5(2):156-161.
Xiong, L. , O'Connor, K. M. and Goswami, M. 1999. Application of the Artificial Neural Network (ANN) in Flood Forecasting on a Karstic Catchment, Conceptual and Neural Network Models, Journal of Hydrology, 321(1-4):344-363.