Verification of MIKE 11-NAM Model for runoff modeling using ANN, FIS, and ARIMA methods in poorly studied basin

Hydrological information is the basis for conducting water balance studies in any region, and surface runoff one of most important hydrological parameters difficult process estimation prediction. This study aims to verification MIKE 11-NAM Model modeling using artificial neural network (ANN), fuzzy inference system (FIS), autoregressive integrated moving average (ARIMA) methods at Al-Jawadiyah hydrometric station on Orontes River Syria. MATLAB was used build models, where many models were built with change all parameters, functions, algorithms that can be used, Minitab ARIMA models. Many prepared addition seasonal effect, comparison results showed an advantage terms evaluation parameters. After that, adopted filling gaps time series area Mike program through method trial error a high number iterative cycles, model calculated values estimated. Still, not good, there significant differences between measured simulated by model, this due lack available data. recommends use intelligence machine learning field prediction