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 , Hosein jamasbi ² , Omid Alipour ³

1 - Assistant Prof. Department of civil engineering, Islamic Azad University, Lahijan Branch, lahijan, Iran *(Corresponding Authours).
2 - Ph.d.Department of civil engineering, Islamic Azad University, Lahijan Branch, lahijan, Iran
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.

References:

1. Ahmadi, M. (2012). Estimating sediment of rivers by RBF and MLP. 11^th hydraulic conference in Iran, Urmia University, IRAN (In Persion).


2. Amutha, R., Porchelvan, P.2011. Seasonal Prediction of Ground Water Levels Using Anfis and Radial Basis Neural Network, International Journal of Geology, Earth and Environmental Sciences .7(3):pp. 34-56


3. Bhattacharya, B., Price R.K.  And Solomatine. D.P.2007.Machine learning approach to modeling sediment transport, Journal of Hydraulic Engineering, 133(4): pp.440-450.


4. Hyuk, P., Jang, K., Zhiqing, Kwon. Hyuk, J.and Lee, J.2009. Prediction debrid Yield from burned watershed: comparison of statistical and artificial neural network models, Journal of American Water resources association, 45, (1).


5. Issazadeh, L.Govay, B.2014.Reservoir Sediment Prediction in Duhok Dam Using Artificial Neural Network and Conventional Methods. Indian Journal of Fundamental and Applied Life Sciences.4: pp.56-67.


6. Mirbagheri, S., and Rajaei, T. 2006. Improve the predict and estimating river suspended load with artificial neoural networks. P 435-443, In: The 7rd Civil Engineering National Conference. Tehran.


7. Najafinejhad, A., Babaei, A., Saniei, E., and Mahmoodi, O. 2010. Comparision of monthly and seasonal suspended load rating curves in several Golestan province rivers. In: The 4rd National Seminar on Erosion and Sediment. Noor, Iran, 6p.


8. Rajaee,T.,V.Nourani,M. and Kisi,.O. 2011. River suspended sediment load prediction: Application of ANN and wavelet conjunction model, Hydrologic Engineering, 16(8):pp.613-627.


9. Rezaei, M., Fereydooni, M.2015.comparative evaluation of adaptive neuro-fuzzy inference system (Anfis) and artificial neural network (ANN) in simulation of suspended sediment load. Indian Journal of Fundamental and Applied Life Sciences.6:pp.78-86.


10. Sadeghi, S.H.R., Saeedi, P., Raeesi, M.B., and Noor, H. 2010. Operation of median groups method in improvement of monthly sediment rating relations. In: The 4th National Seminar on Erosion and Sediment. Noor, Iran, 6p.


11. Varvani, J., Najafinejhad, A., and Mirmoeeni, A. 2008. Correction of sediment rating curve with minimum variance unbiased estimator method. J. Agric. Sci. and Natur. Resour. 15: 1.123-131.


12. Zhou, Y., Lu, X.X., Huang, Y., Zhu, Y.M., 2007. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the upper Yangtze catchment, China.Geomorphology84, 111-125.