ارزیابی مدل‌های هوشمندGPR-PSO و KNN-PSO در برآورد توزیع غلظت رسوبات معلق

Abbaszadeh, H., Daneshfaraz, R., Sume, V., & Abraham, J. 2024. Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions. AQUA—Water Infrastructure, Ecosystems and Society, 73(3), 637-661.‏

Abbaszadeh, H., Norouzi, R., Sume, V., Kuriqi, A., Daneshfaraz, R., & Abraham, J. 2023. Sill role effect on the flow characteristics experimental and regression model analytical. Fluids, 8(8), p. 235.‏

Adnan, R.M., Liang, Z., El-Shafie, A., Zounemat-Kermani, M. and Kisi, O., 2019. Prediction of suspended sediment load using data-driven models. Water, 11(10), p.2060.

Chien, N., 1955. Effects of Heavy Sediment Concentration Near the Bed on Velocity and Sediment Distribution. Missoury River Division, Corps of Engineers, US Army.

Cigizoglu, H.K., 2004. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources, 27(2), pp.185-195.

Dong, X., Yu, Z., Cao, W., Shi, Y. and Ma, Q., 2020. A survey on ensemble learning. Frontiers of Computer Science, 14, pp.241-258.

Gupta, L.K., Pandey, M., Raj, P.A. and Shukla, A.K., 2023. Fine sediment intrusion and its consequences for river ecosystems: a review. Journal of Hazardous, Toxic, and Radioactive Waste, 27(1), p.04022036.

Hassanpour, F., Sharifazari, S., Ahmadaali, K., Mohammadi, S. and Sheikhalipour, Z., 2019. Development of the FCM-SVR hybrid model for estimating the suspended sediment load. KSCE Journal of Civil Engineering, 23, pp.2514-2523.

Hassanzadeh, Y. & Abbaszadeh, H. 2023. Investigating Discharge Coefficient of Slide Gate-Sill Combination Using Expert Soft Computing Models. Journal of Hydraulic Structures, 9(1), pp.63-80.

Hassanzadeh, Y., 2007. Evaluation of sediment load in a natural river. Water International, 32(1), pp.145-154.

Hassanzadeh, Y., Abbaszadeh, H. 2023. Investigating Discharge Coefficient of Slide Gate-Sill Combination Using Expert Soft Computing Models', Journal of Hydraulic Structures, 9(1), pp. 63-80. doi: 10.22055/jhs.2023.43683.1251

Hassanzadeh, Y., Abbaszadeh, H., Abedi, A., & Abraham, J. 2024. Numerical simulation of the effect of downstream material on scouring-sediment profile of combined spillway-gate. AQUA—Water Infrastructure, Ecosystems and Society, jws2024360.‏

Heddam, S., Naghibi, A., Khosravi, K. and Singh, S.K., 2024. Suspended sediment load prediction and tree-based algorithms. In Remote Sensing of Soil and Land Surface Processes (pp. 257-269). Elsevier.

Kaveh, K., Kaveh, H., Bui, M.D. and Rutschmann, P., 2021. Long short-term memory for predicting daily suspended sediment concentration. Engineering with Computers, 37, pp.2013-2027.

Khozani, Z.S., Safari, M.J.S., Mehr, A.D. and Mohtar, W.H.M.W., 2020. An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels. Journal of Hydrology, 584, p.124753.

Marashi, A., Kouchakzadeh, S. & Yonesi, H.A. 2023. Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA. Journal of Hydroinformatics. 25(4), 1312-1328.

Mehri, Y., Nasrabadi, M. and Omid, M.H., 2021. Prediction of suspended sediment distributions using data mining algorithms. Ain Shams Engineering Journal, 12(4), pp.3439-3450.

Mehri, Y., Soltani, J. & Khashehchi, M. 2019. Predicting the coefficient of discharge for piano key side weirs using GMDH and DGMDH techniques. Flow Measurement and Instrumentation, 65, pp. 1-6.

Melesse, A.M., Ahmad, S., McClain, M.E., Wang, X. and Lim, Y.H., 2011. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98(5), pp.855-866.

Nasrabadi, M., Mehri, Y., Ghassemi, A. and Omid, M.H., 2021. Predicting submerged hydraulic jump characteristics using machine learning methods. Water Supply, 21(8), pp.4180-4194.

Nasrabadi, M., Riahi, S., Samadi Borujeni, H. 2014. Evaluation of the Distribution Equations of Suspended Sediment Concentration in Open Channels, Iranian Water Research, 8(1), 175-185 (in Persian).

Omid, M. H. and Nasrabadi, M. 2012. Sedimentation Engineering. Tehran University Press. First Edition. 790 pages (in Persian).

Pal, D. and Ghoshal, K., 2016. Vertical distribution of fluid velocity and suspended sediment in open channel turbulent flow. Fluid Dynamics Research, 48(3), p.035501.

Prandtl, L., 1932. Zur turbulenten Strömung in Rohren und längs Platten. In Ergebnisse der aerodynamischen Versuchsanstalt zu Göttingen Lfg. 4 (pp. 18-29). De Gruyter.

Ribeiro, M.H.D.M. and dos Santos Coelho, L., 2020. Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Applied soft computing, 86, p.105837.

Rouse, H., 1937. Modern conceptions of the mechanics of fluid turbulence. Transactions of the American Society of Civil Engineers, 102(1), pp.463-505.

Samantaray, S. and Sahoo, A., 2022. Prediction of suspended sediment concentration using hybrid SVM-WOA approaches. Geocarto International, 37(19), pp.5609-5635.

Shafai-Bejestan, M. 2008. Theory and Application of Sediment Transport Hydraulics. Shahid Chamran University Press, Ahvaz. 550 pages (in Persian).

Taki, M., Rohani, A., Soheili-Fard, F. and Abdeshahi, A., 2018. Assessment of energy consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models. Journal of cleaner production, 172, pp.3028-3041.

Trojovský, P. and Dehghani, M., 2022. Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), p.855.

Ulke, A., Tayfur, G. and Ozkul, S., 2009. Predicting suspended sediment loads and missing data for Gediz River, Turkey. Journal of Hydrologic Engineering, 14(9), pp.954-965.

Vanoni, V.A., 1946. Transportation of suspended sediment by water. Transactions of the American Society of Civil Engineers, 111(1), pp.67-102.

Zenko, B., Todorovski, L. and Dzeroski, S., 2001, November. A comparison of stacking with meta decision trees to bagging, boosting, and stacking with other methods. In Proceedings 2001 IEEE international conference on data mining (pp. 669-670). IEEE.

Zhu, Y.M., Lu, X.X. and Zhou, Y., 2007. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology, 84(1-2), pp.111-125.

 Zounemat-Kermani, M., Batelaan, O., Fadaee, M. and Hinkelmann, R., 2021. Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology, 598, p.126266.