Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In\nthis study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the\ndaily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layersââ?¬â?input, hidden, and\noutput.The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for\n21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely,\nbackpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed\nto explore the networkââ?¬â?¢s optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect\novertiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict streamflow during\ntesting period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained usingMNN during\ntraining and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively.The predictive uncertainties for both periods\nwere 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff\nrelations.
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