Todini, E. (2001). A bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements. Hydrology and Earth System Sciences. 5 (2), 187-199 Mackay, N. G., Chandler, R. E., Onof, C., and Wheater, H. S. (2001). Disaggregation of spatial rainfall fields for hydrological modeling. Hydrology and Earth System Sciences. 5 (2), 165-173 Rodriguez-Iturbe, I., Cox, D. R., and Isham, V. (1987). Some models for rainfall based on stochastic point processes. Mathematical and Physical Sciences. 410, 269-288 Glasbey, C. A., Cooper, G., and Mcgechan, M. B. (1995). Disaggregation of daily rainfall by conditional simulation from a point-process model. J. of Hydrology. 165, 1-9 Bo, Z., Islam, S., and Eltahir, E. A. B. (1994). Aggregation-disaggregation properties of a stochastic rainfall model. Water Resources Research. 30 (12), 3423-3435 Ormsbee, L. E. (1989). Rainfall disaggregation model for continuous hydrologic modeling. J. of Hydraulic Engineering. 115 (4), 507-525 Cowpertwait, P. S. P. (2001). A continuous stochastic disaggregation model of rainfall for peak flow simulation in urban hydrologic systems. Mathematical Reseaech Letters. 2, 81-88 Shams, S., Abedini M. J., and Asghari K. (2003). Rainfall disaggregation via artificial neural networks. 4th Iranian Hydraulic Conference, Shiraz University, Shiraz. , 1-8 Burian, S. J., Durrans, S. R., Nix, S. J., and Pitt, R. E. (2001). Training artificial neural networks to perform rainfall disaggregation. J. of Hydrologic Engineering. 6 (3), 43-51 Burian, S. J., Durrans, S. R., Tomic, S., Pimmel, R. L., and Wai, C.N. (2000). Rainfall disaggregation using artificial neural networks. J. of Hydrologic Engineering. 5 (3), 299-307 Tantanee, S., Patamatumkul, S., Oki, Sriboonlue, V., and Prempre, T. (2005). Downscaled rainfall prediction model (DRPM) using a unit disaggregation curve (UDC). Hydrology and Earth System Sciences Discussions. 2, 543-568 Fox, N. I., and Collier, C. G. (2000). Physical disaggregation of numerical model rainfall. Hydrology and Earth System Sciences. 4 (3), 419-424 Salas, J. D., Delleur, J. W., Yevjevich, V., and Lane, W. (1988). Applied modeling of hydrologic time series. Water Resources Pub., USA. Luk, K. C., Ball, J. E., and Sharma, A. (2000). A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. of Hydrology. 227 (1), 56-66 Heneker, T. M., Lambert, F., and Kuczera, G. (2001). A point rainfall model for risk-based design. J. of Hydrology. 247 (1), 54-71 Hershenhorn, J., and Woolhiser, D. A. (1987). Disaggregation of daily rainfall. J. of Hydrology. 95, 299-322 Hoang, T. M. T., Rahman, A., Weinmann, P. E., Laurenson, E. M., and Nathan, R. J. (1999). Joint probability description of design rainfalls. Proc. of Water 99 Joint Congress – Brisbane, Australia, Institute of Engineers. , 379-384 Magness, A. L. G., and McCuen, R. H. (2004). Accuracy evaluation of rainfall disaggregation methods. J. of Hydrologic Engineering. 9 (2), 71-77 Rippa, S. (1999). An algorithm for selecting a good value for the parameter C in radial basis function interpolation. Advances in Computational Mathematics. 11, 193-210