River Flow Forecasting using artificial neural network (Shoor Ghaen) - Rezaei, Mohsen; Motlaq, Ahmad Ali Akbari; Mahmouei, Ali Rezvani; Mousavi, Seyed Hojjatollah (Ciência e Natura, 2015)
In our country, most of the rivers located in dry and warm climate areas are seasonal, and many of them have experienced floods. That, along with concerns about scarcity of water resources and the need to control surface water, makes identification, modeling, and simulation of rivers’ behavior, necessary for to long-term planning and proper and rational use of river flows potential. Rainfall phenomenon and the resulting runoff in watersheds, as well as predicting them are of nonlinear system types. Artificial neural networks are able to analyze and simulate phenomena in nonlinear and uncertain system where the relationship between the components and system parameters are not well known or describable. Shoor Ghayen River, with 100 km length is the biggest seasonal river of Qaenat city and the main source of water in Farrokhi storage dam. Therefore, in this study according to the rainfall and runoff statistic of Khonik Olya hydrometric and Ghayen synoptic stations between 1976-1977 and 2010-2011 water years, precipitation phenomena and river runoff was predicted. MATLAB software is used to perform calculations. For modeling artificial neural network, 85 percent of data were used for training the proposed method, the remaining 15% were used for validating the method using 10 neurons, and a network with an error of less than 5% was developed for each month. The maximum correlation in evaluation phase was for April with the value of 0.99, and the minimum was for June and August with a value of 0.92. Overall results indicate optimum performance of artificial neural networks in predicting runoff caused by rainfall. It is also found that better results can be achieved by standardizing the data.