[1] 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.
[2] Chamoun, S., De Cesare, G., Schleiss, A. J. (2017). Venting of turbidity currents approaching a rectangular opening on a horizontal bed.
Journal of hydraulic research, 1–15.
[3] Tayfur, G. (2014). Soft computing in water resources engineering: Artificial neural networks, fuzzy logic and genetic algorithms. WIT Press.
[4] Harrington, S. T., Harrington, J. R. (2013). An assessment of the suspended sediment rating curve approach for load estimation on the Rivers Bandon and Owenabue, Ireland. Geomorphology, 185, 27–38.
[5] Shiau, J. T., Chen, T. J. (2015). Quintile regression-based probabilistic estimation scheme for daily and annual suspended sediment loads.
Water resources management, 29(8), 2805–2818.
[6] Heng, S., Suetsugi, T. (2014). Comparison of regionalization approaches in parameterizing sediment rating curve in ungauged catchments for subsequent instantaneous sediment yield prediction.
Journal of hydrology, 512, 240–253.
[7] Li, X., Nour, M. H., Smith, D. W., Prepasc, A. A. (2010). Neural networks modeling of nitrogen export: model development and application to unmonitored boreal forest watersheds. Environmental technology, 31(5), 495–510.
[8] Chen, X. Y., Chau, K. W. (2016). A hybrid double feed forward Neural Network for suspended sediment load estimation.
Water resources management,
30, 2179–2194.
[9] Shamim, M. A., Hassan, M., Ahmad, S., Zeeshan, M. (2016). A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels.
KSCE journal of civil engineering, 20(2), 971–977.
[10] Thompsona,
J., Sattar,
A., Gharabaghi,
B., Warner,
R. (2016). Event-based total suspended sediment particle size distribution model.
Journal of hydrology, 536, 236-246.
[11] Rajaee, T., Mirbagheri, S. A., Zounemat-Kermani, M., Nourani, V. (2009). Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models.
Science of the total environment,
407(17), 4916-4927.
https://doi.org/10.1016/j.scitotenv.2009.05.016
[12] Kisi, O., Aytac, G. (2010). A machine code-based genetic programming for suspended sediment concentration estimation. Advances in engineering software, 41(8), 939-945.
[13] Boukhrissa, Z. A., Khanchoul, K., Bissonnais, Y. L., Tourki, M. (2013). Prediction of sediment load by sediment rating curve and neural networ.k (ANN) in El Kebir catchment, Algeria. Journal of earth system science, 122(5), 1303–1312.
[14] Sheikhalipour, Z., Hassanpour, F. (2013). Estimation of suspended sediment load using genetic expression programming. Journal of civil engineering and urbanism, 3(2), 292-299.
[15] Abbaspour, B., Haghiabi, A. H. (2015). Comparing the estimation of suspended load using two methods of sediments rating curve and artificial neural network (A Case Study: Cham Anjir Station, Lorestan Province). Journal of environmental treatment techniques, 3(4), 215-222.
[16] Joshi, R., Kumar, K., Adhikari, P. S. (2016). Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier. Hydrology process, 30(2), 1354–1366.
[17] Nivesh, Sh., Kumar, P. (2017). Modelling River suspended sediment load using artificial neural network and multiple linear regression: Vamsadhara River Basin, India. International journal of chemical studies, 5(5), 337-344.
[18] Chen, I. T., Chang, L. C., Chang, F. J. (2017). Exploring the spatio-temporal interrelation between groundwater and surface water by using the Self-Organizing Maps.
Journal of hydrology,
556, 131-142.
[19] Chaudhary, V., Bhatia, R. S., Ahlawat, A. (2014). The self-organizing map learning algorithm with inactive and relative winning frequency of active neurons.
Journal HKIE transactions, 21(1), 62–67.
http://dx.doi.org/10.1080/1023697X.2014.883680
[20] Kakaei Lafdani, E., Moghaddam Nia, A., Ahmadi, A. (2013). Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of hydrology, 478(4), 25-50.
[21] Emamgholizadeh, S. (2012). Neural network modeling of scour cone geometry around outlet in the pressure flushing. Global NEST journal. 14(4), 540-549.
[22] Emamgholizadeh S, Bateni, M. F.M. Jeng D. S. (2013). Artificial intelligence-based estimation of flushing half-cone geometry.
Engineering applications of artificial intelligence,
26(10), 2551-2558.
https://doi.org/10.1016/j.engappai.2013.05.014
[23] Tfwala, S. S., Wang, Y. M. (2016). Estimating sediment discharge using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan.
Water, 8(53), 1-15. https://doi.org/
10.3390/w8020053
[24] Zounemat-Kermani, M., Kişi, O., Adamowski, J., Ramezani-Charmahineh, A. (2016). Evaluation of data driven models for river suspended sediment concentration modeling. Journal of hydrology, 16(2), 1-40.
[25] Ferreira, C. (2006). Gene expression programming: mathematical modeling by an artificial intelligence (Vol. 21). Springer.
[26] Shiri, J., Kisi, O. (2011). Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers and geosciences, 37(10), 1692-1701.
[27] Emamgholizadeh, S. M., Bateni, S.M., Shahsavani, D., Ashrafi, T. Ghorbani, H. (2015). Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). Journal of hydrology, 529, 1590–1600.
[28] Tabatabaei, M., Salehpour Jam, A. (2017). Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network.
Caspian journal of environmental sciences, 15(4), 387-401.
[29] Rashidi, S., Vafakhah, M., Kakaei Lafdani, E., Javadi, M. R. (2016). Evaluating the support vector machine for suspended sediment load forecasting based on gamma test.
Arabian journal of geosciences, 9, 1-15.
[30] Kumar, D., Pandey, A., Sharma, N., Flugel, W. A. (2016). Daily suspended sediment simulation using machine learning approach. CATENA, 138, 77–90.
[31] Samantaray, S., Ghose, D. K. (2018). Evaluation of suspended sediment concentration using descent neural networks. Procedia computer science, 132, 1824–1831.
[32] Azamathulla, H. M., Cuan, Y. C., Ghani, A. b., Chang, C. K. (2013). Suspended sediment load prediction of river systems:GEP approach.
Arabian journal of geosciences, 6, 3469–3480.
[33] 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.