Assessment of GEP and ANN for Predicting Suspended Sediment Load: A Case Study of Ghatoor and Aland Rivers, West Azerbaijan, Iran

Document Type : Original Article


1 Department of Civil Engineering, Faculty of Civil Engineering, University of Tehran, Iran.

2 West Azerbaijan Regional Water Authority, Urmia, Iran.

3 Department of Soil Science, Faculty of Agriculture, Isfahan University of Technology.

4 Master of Science in Power Engineering, Khazar Holding, Mashin Sazi Khorram Abad, Tehran, Iran.



Estimation of the volume of suspended sediment load of rivers, especially when dam constructed on it, is one of the tremendous challenges that civil engineers faced. It is crucial to accurately predict the suspended sediment load to effectively mitigate the negative consequences of this phenomenon. To estimate the total suspended sediment accumulated behind the Aland and Ghatoor dams, two models of artificial intelligence, Gene Expression Programming (GEP) and Artificial Neural Network (ANN), were employed in this study. The performance of these two AI models compared with the traditional method, Sediment Rating Curve (SRC), for estimating the suspended sediment volume using hydrometric stations from 1969 to 2017. Unfortunately, the appropriate data from 2017 to the present is not available from authorities of the West Azerbaijan province, so inevitably, we used the hydrologic records till the end of the year 2017 in this article. Two statistical indices were used to evaluate the models: the coefficient of determination (R-squared) and the Mean Absolute Error (MAE). Based on these indices, the intelligent models performed better than the SRC in estimating the suspended sediment volume. In comparing the GEP and ANN models, the performance criteria show that the ANN model produces better results. For the Ghatoor River, the performance indicators of the ANN model were MAE=993.1 ton/day and R^2=0.910, which is 45% and 43% higher than the GEP model and SRC method, respectively. For Aland River, the performance indicators of the ANN model were MAE=519.2 ton/day and R^2=0.961, which is 12% and 57% higher than the GEP model and SRC method, respectively. In conclusion, for predicting the suspended sediment load in Ghatoor and Aland Rivers, the ANN model can be the best choice for this purpose.


Alijanpour Shalmani, A., Vaez, A.R., Tabatabaei, M.R.J.E.R.R., 2022. Prediction of daily suspended sediment load using the Genetic Expression Programming and Artificial Neural Network models. 10(1): 115-132.
Allawi, M.F., Sulaiman, S.O., Sayl, K.N., Sherif, M., El-Shafie, A., 2023. Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study. Heliyon.
Aytek, A., Ki┼či, Ö.J.J.o.h., 2008. A genetic programming approach to suspended sediment modelling. 351(3-4): 288-298.
Bazoobandi, A., Emamgholizadeh, S., Ghorbani, H.J.E.J.o.E., Engineering, C., 2022. Estimating the amount of cadmium and lead in the polluted soil using artificial intelligence models. 26(3): 933-951.
Bilotta, G.S., Brazier, R.E., 2008. Understanding the influence of suspended solids on water quality and aquatic biota. Water research, 42(12): 2849-2861.
Emamgholizadeh, S., 2012. Neural network modeling of scour cone geometry around outlet in the pressure flushing. Global NEST Journal, 14(4): 540-549.
Emamgholizadeh, S. et al., 2017. Estimation of soil dispersivity using soft computing approaches. Neural Computing and Applications, 28: 207-216.
Emamgholizadeh, S., Bateni, S.M., Nielson, J.R., 2018. Evaluation of different strategies for management of reservoir sedimentation in semi-arid regions: a case study (Dez Reservoir). Lake and Reservoir Management, 34(3): 270-282.
Emamgholizadeh, S., Fathi-Moghdam, M., 2014. Pressure flushing of cohesive sediment in large dam reservoirs. Journal of Hydrologic Engineering, 19(4): 674-681.
Emamgholizadeh, S., Karimi Demneh, R., 2019. A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran. Water Supply, 19(1): 165-178.
Emamgolizadeh, S., Bateni, S., Shahsavani, D., Ashrafi, T., Ghorbani, H., 2015a. Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). Journal of Hydrology, 529: 1590-1600.
Emamgolizadeh, S., Bateni, S., Shahsavani, D., Ashrafi, T., Ghorbani, H.J.J.o.H., 2015b. Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). 529: 1590-1600.
Fathi-Moghadam, M., Emamgholizadeh, S., Bina, M., Ghomeshi, M., 2010. Physical modelling of pressure flushing for desilting of non-cohesive sediment. Journal of Hydraulic Research, 48(4): 509-514.
Fausett, L.V., 2006. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India.
Ferreira, C., 2006. Automatically defined functions in gene expression programming. 21-56.
Ferreira, C., 2006. Gene expression programming: mathematical modeling by an artificial intelligence, 21. Springer.
Gholipoor, M. et al., 2012. The optimization of root nutrient content for increased sugar beet productivity using an artificial neural network.
Horowitz, A.J.J.H.p., 2003. An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. 17(17): 3387-3409.
Jansson, M.B.J.J.o.H., 1996. Estimating a sediment rating curve of the Reventazon river at Palomo using logged mean loads within discharge classes. 183(3-4): 227-241.
Khan, M.A., Stamm, J., Haider, S.J.A.S., 2021. Assessment of soft computing techniques for the prediction of suspended sediment loads in rivers. 11(18): 8290.
Khan, Q., Hayder, G., Al-Zwainy, F.M., 2023. River Water Suspended Sediment Predictive Analytics Using Artificial Neural Network and Convolutional Neural Network Approach: A Review. Sustainability Challenges and Delivering Practical Engineering Solutions: Resources, Materials, Energy, and Buildings: 51-56.
Khosravi, K., Golkarian, A., Melesse, A.M., Deo, R.C., 2022. Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach. Journal of Hydrology, 610: 127963.
Kisi, O., Shiri, J.J.C., Geosciences, 2012. River suspended sediment estimation by climatic variables implication: comparative study among soft computing techniques. 43: 73-82.
Koza, J.R.J.S., computing, 1994. Genetic programming as a means for programming computers by natural selection. 4: 87-112.
McCulloch, W.S., Pitts, W.J.T.b.o.m.b., 1943. A logical calculus of the ideas immanent in nervous activity. 5: 115-133.
Nagy, H., Watanabe, K., Hirano, M.J.J.o.H.E., 2002. Prediction of sediment load concentration in rivers using artificial neural network model. 128(6): 588-595.
Olyaie, E., Banejad, H., Chau, K.-W., Melesse, A.M.J.E.m., assessment, 2015. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. 187: 1-22.
Parhizkar, S., Ajdari, K., Kazemi, G.A., Emamgholizadeh, S., 2015. Predicting water level drawdown and assessment of land subsidence in Damghan aquifer by combining GMS and GEP models. Geopersia, 5(1): 63-80.
Salas, J.D., 1980. Applied modeling of hydrologic time series. Water Resources Publication.
Senthil Kumar, A., Ojha, C., Goyal, M.K., Singh, R., Swamee, P.J.J.o.H.E., 2012. Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms. 17(3): 394-404.
Shamaei, E., Kaedi, M.J.A.S.C., 2016. Suspended sediment concentration estimation by stacking the genetic programming and neuro-fuzzy predictions. 45: 187-196.
Syvitski, J.P., Morehead, M.D., Bahr, D.B., Mulder, T.J.W.r.r., 2000. Estimating fluvial sediment transport: the rating parameters. 36(9): 2747-2760.
Zhang, W., Wei, X., Jinhai, Z., Yuliang, Z., Zhang, Y.J.C.S.R., 2012. Estimating suspended sediment loads in the Pearl River Delta region using sediment rating curves. 38: 35-46.