پیش‌بینی بار معلق رودخانه‌ای با استفاده از شبکه عصبی مصنوعی و الگوریتم‌ ژنتیک مرتب‌سازی غیرغالب

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Ak R., Li Y., Vitelli V., Zio E., Droguett E.L., Jacinto C.M.C. 2013. NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment. Expert Systems with Applications, 40(4): 1205-1212.

Alijanpour Shalmani A., Vaezi A., and Tabatabaei M. 2020. Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed. Watershed Engineering and Management, 12(1): 228-243. (In Persian)

Asadi M., Fathzadeh A., and Taghizadeh Mehrjerdi R. 2017. The effects of the daily, monthly, and annual time scales on the suspended sediment load prediction. Hydrogeomorphology, 4(10): 121-143. (In Persian)

Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Ebrahimi M., Fai C.M., and El-Shafie, A. 2020. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm. Environmental Science and Pollution Research, 27(30): 38094-38116.

Barzegari F., and Dastorani M.T. 2016. Suspended sediment prediction using time series and artificial neural networks models (Case Study: Ghazaghly Station in Gorganroud River). Journal of Watershed Management Research, 6(12), 216-225. (In Persian)

Dastorani M.T., Azimi Fashi K., Talebi A., and Ekhtesasi M.R. 2012. Estimation of suspended sediment using artificial neural network (Case Study: Jamishan Watershed in Kermanshah). Journal of Watershed Management Research, 3(6): 61-74. (In Persian)

Emami S., Choopan Y., and Parsa J. 2021. Comparising performance of meta-heuristic algorithms with the sediment rate curve (Case Study: Zarrineh Rood River). Watershed Engineering and Management, 13(1): 43-54. (In Persian)

Gharehchopogh F.S., and Gholizadeh H. 2019. A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 48: 1-24.

Gupta D., Hazarika B.B., Berlin M., Sharma U.M., and Mishra K. 2021. Artificial intelligence for suspended sediment load prediction: a review. Environmental Earth Sciences, 80(9): 346.

Halabian A., Javari M., Akbari Z., and Akbari G. 2017. Evaluating the performance of decision tree model in estimating the suspended sediments of river (A case study on the Basin of Meimeh River). Geography And Development Iranian Journal, 15(49): 81-96. (In Persian)

Hayatzadeh M., Chezgi J., and Taghi Dastorani M. 2015. Evaluation of sediments using rating curve and artificial neural network methods by combining morphological parameters of basin (case study: bagh abbas basin). Journal of Water and Soil Science, 19(72): 217-228. (In Persian)

Imamgholizadeh S., Karimi Demneh R., and Azhdari K. 2016. Comparison of conventional methods for estimation of suspended sediment load of karkheh river by gene expression programming approach. Geography And Development Iranian Journal, 14(45): 121-140. (In Persian)

Keihani A., Akhondali A.M., and Fathian H. 2021. Uncertainty analysis of svm model parameters for estimating suspended and bed sediment load at sierra station in karaj by monte-carlo simulation method. Iranian Journal of Soil and Water Research, 52(1): 195-212. (In Persian)

Khazaei Moughani S., Najafinejad A., Azimmohseni M., and Berdi Sheikh V. 2013. Spatial and seasonal variation of suspended sediment in different stations of gorganroud river. golestan province. Journal of Watershed Management Research, 4(7): 1-15. (In Persian)

Khazaie Poul A., and Talebi A. 2013. Investigation of possibility of suspended sediment prediction using a combination of sediment rating curve and artificial neural network (case study: ghatorchai river, yazdakan bridge). Quarterly Journal of Environmental Erosion Researches, 3(1): 73-82. (In Persian)

Kheirfam H., and Mokarram-Kashtiban S. 2018. A regional suspended load yield estimation model for ungauged watersheds. Water Science and Engineering, 11(4): 328-337

Khorshiddoost A., Feyzolahpour M., and Sadrafshary S. 2015. Assessing the capability of adaptive neuro fuzzy interference system (anfis) in estimating the amount of suspended sediment load and its comparison with two models of artificial neutral fuzzy inference system case study: zarine rood, south east basin of urmia lake. Geography And Development Iranian Journal, 13(41): 185-200. (In Persian)

Kia E., Emadi A., and Fazlola R. 2015. Investigation for application of adaptive neuro-fuzzy inference system (anfis) in babolroud suspended sediment load estimation. Journal of Watershed Management Research, 6(11): 15-23. (In Persian)

Najafinejad A., Mardian M., Varvani J., and Sheikh V.B. 2011. Performance evaluation of correction factors in optimization of sediment rating curve (case study: kamal saleh dam watershed, markazi province, iran). Journal of Water and Soil Conservation,  18(2): 105-122.

Nikkami D., and Shadfar S. 2021. Soil erosion mapping in sediment gauged watersheds of Iran. Watershed Engineering and Management, 13(2): 479-496.

Panahi F., Ehteram M., and Emami M. 2021. Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test. Environmental Science and Pollution Research, 28(35): 1-21.

Rahgoshay M., Feiznia S., Arian M., and Hashemi S.A.A. 2019. Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm. Arabian Journal of Geosciences, 12(9): 277.

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Razavizadeh S., Kavian A., and Vafakhah M. 2014. Estimation of suspended sediment discharge by optimal structure of artificial neural network in taleghan watershed. Journal of Water and Soil Science, 18(68): 79-88. (In Persian)

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Tabatabaei M., Salehpour Jam A., and Mosaffaie J. 2020. Improvement of the efficiency of artificial neural network model in suspended sediment simulation using particle swarm optimization algorithm. Watershed Engineering and Management, 12(3): 756-770. (In Persian)

Tabatabaei M.R, Solaimani K., Habibnejad Roshan M., and Kavian A. 2014. Estimation of daily suspended sediment concentration using artificial neural networks and data clustering by self-organizing map (case study: sierra hydrometry station- karaj dam watershed). Journal of Watershed Management Research, 5(10): 98-116. (In Persian)

Varvani J., Gholi Ebrahimi N., Yosefi M., Shadmani A.R., and Nikche Farahani S. 2016. A performance evaluation of neuro-fuzzy and regression methods in estmation of sediment load of river. Watershed Management Research (Pajouhesh & Sazandegi), 28(4): 104-113. (In Persian)

Yilmaz B., Aras, E., Kankal M., and Nacar S. 2019. Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches. Acta Geophysica, 67(6): 1693-1705.

Yosefi M., and Poorshariaty R. 2015. Suspended sediment estimation using neural network and algorithms assessment (case study: lorestan province). Journal of Watershed Management Research, 5(10): 85-97. (In Persian)

Yousefi M., and Barzegari F. 2014. Estimation of suspeded sediment using decision tree method and copmarision the results with rating curve method (Case study: Rivers in Lorestan Province). Watershed Management Research (Pajouhesh & Sazandegi), 27(3): 187-195. (In Persian)

Zare M., Pourghasemi H.R., Vafakhah M., and Pradhan B. 2013. Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences, 6(1): 2873-2888.

Zhao G., Pang B., Xu Z., and Xu L. 2020. A hybrid machine learning framework for real-time water level prediction in high sediment load reaches. Journal of Hydrology, 581(1): 1-10.