Akbari, M., Salmasi, F., Arvanaghi, H., Karbasi, M. and Farsadizadeh, D. (2019). Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir. Water Resources Management, 33, 3929-3947.
Azimi, H., Bonakdari, H. and Ebtehaj, I. (2019). Design of radial basis function‑based support vector regression in predicting the discharge coefficient of a side weir in a trapezoidal channel. Applied Water Science, 9, 78-90.
Dabling M.R. (2014). Nonlinear weir hydraulics. (M.Sc. Thesis), Utah state university, Logan, Utah.
Dabling, M.R. and Tullis, B.P. (2012). Piano key weir submergence in channel applications. Journal of Hydraulic Engineering, ASCE, 138(7), 661-666.
Danandehmehr, A. and Majdzadeh Tabatabai, M.R. (2010). I Prediction of Daily Discharge Trend of River Flow Based on Genetic Programming. Journal of Water and Soil, 24(2), 325-333. (In Persian)
Fathizad, H., Safari, A., Bazgir, M. and Khosravi, Gh. (2017). Evaluation of SVM with Kernel method (linear, polynomial, and radial basis) and neural network for land use classification. Iranian Journal of Range and Desert Research, 23(4), 729-743. (In Persian)
Ferreira, C. (2001). Gene expression programming a new adaptive algorithm for solving problems. Complex Systems, 13(2), 87-129.
Fuladipanah, M., Majedi Asl, M. and Haghgooyi, A. (2020). Application of intelligent algorithm to model head-discharge relationship for submerged labyrinth and linear weirs. Journal of Hydraulics, DOI: 10.30482/JHYD.2020.232388.1461. (In Persian)
Ghobadian, R., Ghorbani, M.A. and Khalaj, M. (2013). Comparison of Performance of Dynamic Wave and Gen Expression Programming Methods to River flood routing. Journal of Water and Soil, 27(3), 592-602. (In Persian)
Granata, F., Nunno, F.D., Gargano, R. and Marinis, G. (2019). Equivalent Discharge Coefficient of Side Weirs in Circular Channel-A Lazy Machine Learning Approach. Water, 11(2406), 1-19.
Haghiabi, A.H., Parsaie, A. and Ememgholizadeh, S. (2018). Prediction of discharge coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system. Alexandria Engineering Journal, 57, 1773-1782.
Henderson, F.M. (1966). Open chnnael flow. MacMilan Publication, New York, 576P.
Hu, J. and Zheng, K. (2015). A novel support vector regression for data set with outliers. Applied Soft Computing, 31, 405-411.
Kamaei Abbasi, B., Kamaei abbasi, S.R. and Heidarnejad, M. (2020). Experimental study of Discharge Coefficient in Two-Cycle Piano Key Weirs. Iranian Journal of Irrigation and Drainage, 13(73), 10-20. (In Persian)
Kumar, M., Sihag, P., Tiwari, N.K. and Ranjan, S. (2020). Experimental study and modelling discharge coefficient of trapezoidal and rectangular piano key weirs. Applied Water Science, 10, 43-52.
Kumar, S., Ahmad, Z., Mansoor, T. and Himanshu, S.K. (2012). Discharge Characteristics of Sharp Crested Weir of Curved Plan-form. Research Journal of Engineering Sciences, 1(4), 16-20.
Lempérière, F., Vigny, J.P. and Ouamane, A. (2011). General comments on Labyrinths and Piano Key Weirs: The past and present. Labyrinth and Piano Key Weirs-PKW, CRC press, London.
Majedi Asl, M. and Fuladipanah, M. (2018). Application of the Evolutionary Methods in Determining the Discharge Coefficient of Triangular Labyrinth Weirs. Journal of Water and Soil Science (Science and Technology of Agriculture and Natural Resources), 22(4), 279-290. (In Persian)
, Y., Esmaeili, S., Soltani, J., Saneie, M. and Rostami, M. (2018). Evaluation of SVM and nonlinear regression models for predicting the discharge coefficient of side piano key weirs in irrigation and drainage networks. Iranian Journal of Irrigation and Drainage, 12(70), 994-1003. (In Persian).
Norouzi, R., Daneshfaraz, R. and Ghaderi, A. (2019). Investigation of discharge coefficient of trapezoidal labyrinth weirs using artificial neural networks and support vector machines. Applied Water Science, 9(148), 1-10.
Novak, G., Kozelj, D., Steinman, F. and Bajcar, T. (2013) Study of flow at side weir in narrow flume using visualization techniques. Flow Measurment Instrument, 29, 45-51.
Olyaie, E., Heydari, M., Banejad, H., and Chau, K.W. (2019a). A laboratory investigation on the potential of computational intelligence approaches to estimate the discharge coefficient of piano key weir. Journal of Rehabilitation in Civil Engineering, 7(1), 42-61.
Olyaie, E., Banejad, H., and Heydari, M. (2019b). Estimating discharge coefficient of PK-weir under subcritical conditions based on high-accuracy machine learning approaches. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 43(1), 89-101.
Parsaie, A., Haghiabi, A.H., Emamgholizadeh, S. and Azamathulla, H.M. (2019). Prediction of discharge coefficient of combined weir-gate using ANN, ANFIS and SVM. International journal of Hydrology Science and Technology, 9(4), 412-430.
Roushangar, K., Majedi Asl, M. and Alami, M.T. (2018). Experimental Evaluation of Hydraulic Performance of Modified Piano Key Weirs. Journal of water and soil science, 28(3), 93-104. (In Persian)
Roushangar, K., Alami, M.T., Shiri, J. and Majedi Asl, M. (2017). Determining discharge coefficient of labyrinth and arced labyrinth weirs using support vector machine. Journal of Hydrology Research, 49(3), 924-938.
Safarzadeh, A. and Norouzi, B. (2018). Experimental and Numerical Study on Hydraulics of the Piano-key Weirs with Modified Keys. Dam and Hydroelectric Power plant, 5(16), 36-47. (In Persian)
Safarzadeh, A., Khayat Rostami, S. and Khayat Rostami, B. (2019). Investigation on the effects of water head on discharge distribution and streamlines pattern over the Asymmetric Piano key weirs. Journal of Hydraulics, 14(1), 1-17. (In Persian)
Salarijazi, M., Ghorbani, K., Sohrabian, E. and Abdolhosseini, M. (2016). Prediction of Daily Stream-flow Using Data Driven Models. Iranian Journal of Irrigation and Drainage, 4(10), 479-488. (In Persian)
Solgi, A., Zarei, H. and Golabi, M.R. (2017). Performance assessment of gene expression programming model using data preprocessing methods to modeling river flow. Journal of Water and Soil Conservation, 24(2): 185-201. (In Persian)
Zhou, Q., Zhou, H., Zhou, Q., Yang, F., Luo, L. and Li, T. (2015). Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory. Appllied Soft Computing, 36, 368–374.
Zounemat-Kermani, M. and Mahdavi-Meymand, A. (2019). Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs. Journal of hydrology, 569, 12-21.
Villemonte, D. (1947). Submerged weir discharge studies. Engineering News Record, 866 p.