%0 Journal Article
%T Comparison of Neuro Fuzzy, Neural Network Artificial and Statistical Methods for Estimating Suspended Load Rivers (Case Study: Taleghan Basin Upstream)
%J Journal of Range and Watershed Managment
%I دانشکده منابع طبیعی دانشگاه تهران
%Z 5044-2008
%A Zoratipour, Amin
%D 2016
%\ 05/21/2016
%V 69
%N 1
%P 65-78
%! Comparison of Neuro Fuzzy, Neural Network Artificial and Statistical Methods for Estimating Suspended Load Rivers (Case Study: Taleghan Basin Upstream)
%K Neuro fuzzy
%K Artificial Neural Network
%K Suspended Load
%K Statistical Method
%K Taleghan
%R 10.22059/jrwm.2016.61734
%X Abstract
Estimation of fine suspended load rivers is important in designing reserves, transition volume ofsediment, and estimating lake pollution. Thus, some methods are needed for determining damagescaused by sedimentations in environment and determining its effects on the watersheds. There aremany methods for estimating suspended load, one of these methods that solves the problems ofsediment discharge and can predict it is using Neuro fuzzy or ANFIS (Adaptive Network FuzzyInference System), and ANN (Artificial Neural Network) methods. These make a function betweensediment and simultaneous discharge by use of different algorithms. The goal of this research iscomparing the effectiveness of Neuro fuzzy, neural network artificial and statistical methods forestimating suspended load river in Glinak station of Taleghan Basin. It was found out thatsuspended load estimations of Nero fuzzy method with MAE 1006 ton/day, and correlationefficiency (R) 77%, RMSE 2621 ton/day and Nash-Sutcliff error (NS) 0.51 is better than NeuralNetwork Artificial and Statistical methods and Artificial Neural Network method rather thanStatistical Method are more proper. Also, contracting both neural networks artificial to fuzzy lawscan be illustrated better than other methods, variation of sediment Load River. One more merit ofthis method is that it is not sensitive to few errors in early statistical data and this fact enables betterestimation of neural network model in comparison with statistical model. Finally, Neuro fuzzymethod works better as the percent of train data to test data increases.
%U https://jrwm.ut.ac.ir/article_61734_bb3aba3afb5589bbc7caeb260ddf89a7.pdf