Evaluation of Suspended Sediment Load by Sediment Rating Curves and Comparing with Artificial Neural Network and Regression Methods (Case study: Babolrud River Mazandaran Province)

Subject Areas : sediment

Alireza  Mardookhpour ¹ (Assistant Prof. Department of civil engineering, Islamic Azad University, Lahijan Branch, lahijan, Iran *(Corresponding Authours).)
Hosein  jamasbi ² (Ph.d.Department of civil engineering, Islamic Azad University, Lahijan Branch, lahijan, Iran)
Omid  Alipour ³ (Ms.C. Department of civil engineering, Islamic Azad University, Lahijan Branch, lahijan, Iran.)

Received: 2016-11-21 Accepted : 2017-05-10 Published : 2020-01-21

Keywords: Regression, Artificial Neural Network, sediment rating curves, Sediment, Babolrud River,

Abstract :

Background and Objective: In this research the object is prediction of suspended sediment load by and artificial neural network (ANN), Sediment Rating Curves (SRC) and regression methodfor BabolrudRiver in Mazandaran province. Method: The inputs conclude discharge and the output is sediments concentration in time series. The input and output of river have positive procedure for (1979-2013) and 75% of data utilized for training and 25% for tests. For training the network, data that recognize issue conditions were selected and some data for testing, Findings: The results show the concentration of sediment suspended load derived artificial neural network and is close together and regression coefficient is 92.8%, while regression coefficient is 83% for sediment rating curves and 90% for statistical method respectively. Discussion and Conclusion: In conclusion, artificial neural network (ANN) has more workability and flexibility for prediction of suspended sediment load to sediment rating curves and statistical methods.

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