Climatology
Ali Mohammad Khorshiddoust; Fariba Esfandeyari; Seyed Asaad Hosseini; Parvaneh Dolatkhah
Volume 22, Issue 65 , November 2018, , Pages 141-162
Abstract
One of the new techniques in the field of predicting hydrological and geomorphologic processes is artificial neural network from the components of artificial intelligence which are trying to implement the amazing features of human brain in an artificial system and are powerful tools in the field of modeling ...
Read More
One of the new techniques in the field of predicting hydrological and geomorphologic processes is artificial neural network from the components of artificial intelligence which are trying to implement the amazing features of human brain in an artificial system and are powerful tools in the field of modeling and predicting geomorphologic parameters and in this study have been used for the prediction of sediment in Aras basin. For this purpose was used information of discharge, sedimentation and prediction monthly on Borran hydrometric station located in the Basin of Darreh Roud that is from the main sub basin of Aras river in Moghan plain during the period of 34 years (water year of 53-54 to 86-87). So that the discharge and precipitation rate as inputs to the neural network and sediment was considered the output of network. For this purpose used the facilities and functions available in programming environment MATLAB / 2010 and SPSS / 21 software. Then models were evaluated through statistical parameters such as the determination coefficient, root mean square error, mean square error, mean absolute error, correlation coefficient and also mean percentage relative error. The results, in addition to confirming the capability of artificial neural network model, showed that, there is good correspondence between predicted values and observed data. So that the error mean of this model with the observed data is 0.9 and correlation coefficient is 0.99 which is significant at 0.01.The results of this study showed that the artificial neural. Network model has more accuracy in the estimation of sediment at the investigated basin. The results can be useful in planning and management of water and watersheds and natural resource management, especially in agriculture, industry, drinking and Forecast of Reservoir Sedimentation
