Aminpour, Y., and Farhoudi, J. (2015). Investigation of local scour phenomenon downstream of stilling basins in the presence of steppe spillway. Journal of Hydraulics, (9)4. 25–38. (In Persian)
Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Chen, S. T., and Yu, P. S. (2007). Real-time probabilistic forecasting of flood stages. Journal of Hydrology, 340(1-2), 63-77.
Daneshfaraz, R., Bagherzadeh, M., Esmaeeli, R., Norouzi, R., & Abraham, J. (2021). Study of the performance of support vector machine for predicting vertical drop hydraulic parameters in the presence of dual horizontal screens. Water Supply, 21 (1): 217–231.
Daneshfaraz, R., Mehrivar, E., Aminvash, E., & Rezaie, M. (2025). Experimental investigation of scouring parameters of the downstream bed of simple and gabion rostral drops. AQUA - Water Infrastructure, Ecosystems and Society; 74 (4): 335–348.
Foddis, M. L., Ackerer, P., Montisci, A. and Uras, G. (2015), Ann-Based Approach for the Estimation Aquifer Pollutant Source Behaviour, Water Science and Technology, Water Supply, 15(6), 1285-1294.
Göğüş, M., Defne, Z., and Özkandemir, V. (2006). Broad-crested weirs with rectangular compound cross sections. Journal of Irrigation and Drainage Engineering, (132) 3, 272–280.
Govindaraju, R. S. (2000), Artificial Neural Networks in Hydrology. Ii: Hydrologic Applications, Journal of Hydrologic Engineering, 5, 124-137.
Haykin, S. and Lippmann, R. (1994), Neural Networks, a Comprehensive Foundation, International Journal of Neural Systems, 5, 363-364.
Jang, J.-S. R., Sun, C.-T. and Mizutani, E. (1997), Neuro-Fuzzy and Soft Computing; a Computational Approach to Learning and Machine Intelligence, Prentice-Hall.
Khorami, E., Heidari, M., and Ghobadian, R. Estimation of downstream scour in non-cohesive materials and sensitization of factors affecting it in ski-jump using neural network and laboratory model. Irrigation and Drainage Structures Engineering Research, (23)86, 111-132. (In Persian)
Li, X. and Tsai, F. T.-C. (2009), Bayesian Model Averaging for Groundwater Head Prediction and Uncertainty Analysis Using Multimodel and Multimethod, Water resources research, 45(9).
Martínez, J., Reca, J., Morillas, M. T., and López, J. G. (2005). Design and calibration of a compound sharp-crested weir. Journal of Hydraulic Engineering, (131)2, 112–116.
Majedi-Asl, M., Omidpour Alavian, T., Seyfari, Y., & Kouhdaragh, M. (2024). Modeling of discharge Coefficient of nonlinear weirs with QNET and SVM methods, Journal of Hydraulic Structures, 10(2): 30-45.
Mousavi, S. (2023). Estimating the scour depth of slope control structures with sharp-crested weir using artificial intelligence models. Water Resources, (15)55, 105-118. (In Persian)
Nourani, V., Mousavi, S., Sadikoglu, F., & Singh, V. P. (2017). Experimental and AI-based numerical modeling of contaminant transport in porous media. Journal of contaminant hydrology, )205(, 78-95.
Nourani, V., Mogaddam, A. A. and Nadiri, A. O. (2008), An Ann-Based Model for Spatiotemporal Groundwater Level Forecasting, Hydrological Processes, (22), 5054-5066.
Obaida A., Khattab N., & Mohammed A (2023). Scour depth downstream sharp-crested weir. Journal of Engineering and Applied Science, 70(1), 1-11.
Singh, R. M. and Datta, B. (2007), Artificial Neural Network Modeling for Identification of Unknown Pollution Sources in Groundwater with Partially Missing Concentration Observation Data, Water Resources Management, (21), 557-572.
Rehbock, T.(1929), Discussion of Precise Measurements, Trans of ASCE. 93: p. 1143- 1162.
Roshanghar, K., & Rohparvar, B. (2013). Evaluation of Artificial Intelligence Systems for Simulation of Bridge Piers Scouring in Cohesive Soils. Water and Soil Science, 23(3), 169-182. (In Persian)