forecasting the northern karon rivers discharge by using artificial neural network

flow river forecasting plays an important role in designing, management, and utilization of water resources. the main object of the research is to study the possibility of forecasting and monitoring the northern karon rivers (armand and bazfat) discharge by using modern simulation methods. in this research, armand and bazoft discharge variations was investigated. in this regard, monthly data of soi, nao, and enso in the regions of nino3, nino3.4, nino4, and nino1+2 for the period of 1968 to 2007 were collected from the national center environmental prediction (ncep). daily discharge data of armand and morgak hydrometric stations were prepared from niro ministry data center. in order to have the optimal designing of artificial neural network architecture, for discharge river forecasting on the basis of climatic signals, genetic algorithm method was used. the results show that enso signals in nino1+2 and nino3 regions are the most effective signals on armand and bazfat discharge variations. thus, enso signals can be used for forecasting the northern karon rivers discharge.