[
Abbasi, S., Fatemi, S., Ghaderi, A., Di Francesco, S., 2021. The effect of geometric parameters of the antivortex on a triangular labyrinth side weir. Water, 13, 1. http://dx.doi.org/10.3390/w13010014
]Search in Google Scholar
[
Agaccioglu, H., Yüksel, Y., 1998. Side-weir flow in curved channels. J. Irrig. Drain. Eng., 124, 3, 163–175. http://dx.doi.org/10.1061/(ASCE)0733-9437(1998)124:3(163)
]Search in Google Scholar
[
Ahmed, M.H., Lin, L.-S., 2021. Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique. J. Hydrol., 597, 1–12. http://dx.doi.org/10.1016/j.jhydrol.2021.126213
]Search in Google Scholar
[
Akbari, M., Salmasi, F., Arvanaghi, H., Karbasi, M., Farsadizadeh, D., 2019. Application of Gaussian process regression model to predict discharge coefficient of gated piano key weir. Water Resour. Manage., 33, 11, 3929–3947. http://dx.doi.org/10.1007/s11269-019-02343-3
]Search in Google Scholar
[
Anandhi, A., Srinivas, V.V., Nanjundiah, R.S., Nagesh Kumar, D., 2008. Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. International Journal of Climatology, 28, 3, 401–420. http://dx.doi.org/https://doi.org/10.1002/joc.1529
]Search in Google Scholar
[
Azamathulla, H.M., Haghiabi, A.H., Parsaie, A., 2016. Prediction of side weir discharge coefficient by support vector machine technique. Water Supply, 16, 4, 1002–1016. http://dx.doi.org/10.2166/ws.2016.014
]Search in Google Scholar
[
Bagheri, S., Kabiri-Samani, A.R., Heidarpour, M., 2014. Discharge coefficient of rectangular sharp-crested side weirs. Part II: Domínguez’s method. Flow Meas. Instrum., 35, 116–121. http://dx.doi.org/10.1016/j.flowmeasinst.2013.10.006.
]Search in Google Scholar
[
Bhuiyan, M.A.E., Nikolopoulos, E.I., Anagnostou, E.N., Quintana-Seguí, P., Barella-Ortiz, A., 2018. A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula. Hydrol. Earth Syst. Sci., 22, 2, 1371–1389. http://dx.doi.org/10.5194/hess-22-1371-2018
]Search in Google Scholar
[
Bonakdari, H., Ebtehaj, I., Samui, P., Gharabaghi, B., 2019. Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine. Water Resour. Manage., 33, 11, 3965–3984. http://dx.doi.org/10.1007/s11269-019-02346-0
]Search in Google Scholar
[
Bonakdari, H., Zaji, A.H., Shamshirband, S., Hashim, R., Petkovic, D., 2015. Sensitivity analysis of the discharge coefficient of a modified triangular side weir by adaptive neuro-fuzzy methodology. Meas., 73, 74–81. http://dx.doi.org/10.1016/j.measurement.2015.05.021
]Search in Google Scholar
[
Borghei, S.M., Jalili, M.R., Ghodsian, M., 1999. Discharge coefficient for sharp-crested side weir in subcritical flow. J. Hydraul. Eng., 125, 10, 1051–1056. http://dx.doi.org/10.1061/(ASCE)0733-9429(1999)125:10(1051)
]Search in Google Scholar
[
Borghei, S.M., Nekooie, M.A., Sadeghian, H., Jalili Ghazizadeh, M.R., 2013. Triangular labyrinth side weirs with one and two cycles. Proc. Inst. Civ. Eng. Water Manage., 166, 1, 27–42. http://dx.doi.org/10.1680/wama.11.00032
]Search in Google Scholar
[
Bowden, G.J., Maier, H.R., Dandy, G.C., 2005. Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. J. Hydrol., 301, 1, 93–107. http://dx.doi.org/10.1016/j.jhydrol.2004.06.020
]Search in Google Scholar
[
Brabanter, K.D., Brabanter, J.D., Suykens, J.A.K., Moor, B.D., 2011. Approximate confidence and prediction intervals for least squares support vector regression. IEEE Trans. Neural Networks, 22, 1, 110–120. http://dx.doi.org/10.1109/TNN.2010.2087769
]Search in Google Scholar
[
Breiman, L., 2001. Random forests. Mach. Learn., 45, 1, 5–32. http://dx.doi.org/10.1023/A:1010933404324
]Search in Google Scholar
[
Cartwright, H.M., 2015. Artificial Neural Networks. Springer, New York.
]Search in Google Scholar
[
Cheong, H.F., 1991. Discharge coefficient of lateral diversion from trapezoidal channel. J. Irrig. Drain. Eng., 117, 4, 461–475. http://dx.doi.org/10.1061/(ASCE)0733-9437(1991)117:4(461)
]Search in Google Scholar
[
Coleman, H.W., Steele, W.G., 2009. Experimentation, Validation, and Uncertainty Analysis for Engineers. Wiley, New York, NY, USA.
]Search in Google Scholar
[
Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn., 20, 3, 273–297. http://dx.doi.org/10.1007/BF00994018
]Search in Google Scholar
[
Ebtehaj, I., Bonakdari, H., Gharabaghi, B., 2018. Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Meas., 116, 473–482. http://dx.doi.org/10.1016/j.measurement.2017.11.023
]Search in Google Scholar
[
Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., Khoshbin, F., 2015. GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Eng. Sci. Technol. Int. J., 18, 4, 746–757. http://dx.doi.org/10.1016/j.jestch.2015.04.012
]Search in Google Scholar
[
Emiroglu, M.E., Agaccioglu, H., Kaya, N., 2011. Discharging capacity of rectangular side weirs in straight open channels. Flow Meas. Instrum., 22, 4, 319–330. http://dx.doi.org/10.1016/j.flowmeasinst.2011.04.003
]Search in Google Scholar
[
Francke, T., López-Tarazón, J.A., Schröder, B., 2008. Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests. Hydrol. Process., 22, 25, 4892–4904. http://dx.doi.org/10.1002/hyp.7110
]Search in Google Scholar
[
Gholami, A., Bonakdari, H., Ebtehaj, I., Mohammadian, M., Gharabaghi, B., Khodashenas, S.R., 2018. Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing. Meas., 121, 294–303. http://dx.doi.org/10.1016/j.measurement.2018.02.070
]Search in Google Scholar
[
Granata, F., de Marinis, G., Gargano, R., Tricarico, C., 2013. Novel approach for side weirs in supercritical flow. J. Irrig. Drain. Eng., 139, 8, 672–679. http://dx.doi.org/10.1061/(ASCE)IR.1943-4774.0000600
]Search in Google Scholar
[
Haddadi, H., Rahimpour, M., 2012. A discharge coefficient for a trapezoidal broad-crested side weir in subcritical flow. Flow Meas. Instrum., 26, 63–67. http://dx.doi.org/10.1016/j.flowmeasinst.2012.04.002
]Search in Google Scholar
[
Hager, W., 1987. Lateral outflow over side weirs. J. Hydraul. Eng., 113, 4, 491–504. http://dx.doi.org/10.1061/(ASCE)0733-9429(1987)113:4(491)
]Search in Google Scholar
[
Hu, Z., Karami, H., Rezaei, A., DadrasAjirlou, Y., Piran, M.J., Band, S.S., Chau, K.-W., Mosavi, A., 2021. Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows. Eng. Appl. Comput. Fluid Mech., 15, 1, 1002–1015. http://dx.doi.org/10.1080/19942060.2021.1934546
]Search in Google Scholar
[
Hussain, A., Shariq, A., Danish, M., Ansari, M., 2021. Discharge coefficient estimation for rectangular side weir using GEP and GMDH methods. Adv. Comput. Des., 6, 2, 135–151. http://dx.doi.org/10.12989/acd.2021.6.2.135
]Search in Google Scholar
[
Jalili, M.R., Borghei, S.M., 1996. Discussion: Discharge coefficient of rectangular side weirs. J. Irrig. Drain. Eng., 122, 2, 132–132. http://dx.doi.org/10.1061/(ASCE)0733-9437(1996)122:2(132)
]Search in Google Scholar
[
Johnson, P.A., Ayyub, B.M., 1996. Modeling uncertainty in prediction of pier scour. J. Hydraul. Eng., 122, 2, 66–72. http://dx.doi.org/10.1061/(ASCE)0733-9429(1996)122:2(66)
]Search in Google Scholar
[
Karbasi, M., Jamei, M., Ahmadianfar, I., Asadi, A., 2021. Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms. Sci. Rep., 11, 1, 19784. http://dx.doi.org/10.1038/s41598-021-99166-3
]Search in Google Scholar
[
Kaya, N., Emiroglu, M.E., Agaccioglu, H., 2011. Discharge coefficient of a semi-elliptical side weir in subcritical flow. Flow Meas. Instrum., 22, 1, 25–32. http://dx.doi.org/10.1016/j.flowmeasinst.2010.11.002
]Search in Google Scholar
[
Kilic, Z., Emin Emiroglu, M., 2022. Study of hydraulic characteristics of trapezoidal piano key side weir using different approaches. Water Supply, 22, 8, 6672–6691. http://dx.doi.org/10.2166/ws.2022.264
]Search in Google Scholar
[
Kisi, O., Ozkan, C., 2017. A new approach for modeling sedimentdischarge relationship: Local weighted linear regression. Water Resour. Manage., 31, 1, 1–23. http://dx.doi.org/10.1007/s11269-016-1481-9
]Search in Google Scholar
[
Liao, K.-W., Chien, F.-S., Ju, R.-J., 2019. Safety evaluation of a water-immersed bridge against multiple hazards via machine learning. Appl. Sci., 9, 15, 3116. http://dx.doi.org/10.3390/app9153116
]Search in Google Scholar
[
Liu, Y., Guo, J., Wang, Q., Huang, D., 2016. Prediction of filamentous sludge bulking using a state-based Gaussian processes regression model. Sci. Rep., 6, 1, 31303. http://dx.doi.org/10.1038/srep31303
]Search in Google Scholar
[
Maranzoni, A., Pilotti, M., Tomirotti, M., 2017. Experimental and numerical analysis of side weir flows in a converging channel. J. Hydraul. Eng., 143, 7, 1–15. http://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0001296
]Search in Google Scholar
[
Meinshausen, N., Ridgeway, G., 2006. Quantile regression forests. J. Mach. Learn. Res., 7, 6, 983–999.
]Search in Google Scholar
[
Mohammed, A.Y., Golijanek-Jędrzejczyk, A., 2020. Estimating the uncertainty of discharge coefficient predicted for oblique side weir using Monte Carlo method. Flow Meas. Instrum., 73, 1–15. http://dx.doi.org/10.1016/j.flowmeasinst.2020.101727
]Search in Google Scholar
[
Momeni, E., Dowlatshahi, M.B., Omidinasab, F., Maizir, H., Armaghani, D.J., 2020. Gaussian process regression technique to estimate the pile bearing capacity. Arabian J. Sci. Eng., 45, 10, 8255–8267. http://dx.doi.org/10.1007/s13369-020-04683-4
]Search in Google Scholar
[
Nateghi, R., Guikema, S.D., Quiring, S.M., 2014. Forecasting hurricane-induced power outage durations. Nat. Hazard., 74, 3, 1795–1811. http://dx.doi.org/10.1007/s11069-014-1270-9
]Search in Google Scholar
[
Nourani, B., Arvanaghi, H., Salmasi, F., 2021. A novel approach for estimation of discharge coefficient in broad-crested weirs based on Harris Hawks Optimization algorithm. Flow Meas. Instrum., 79, 1–13. http://dx.doi.org/10.1016/j.flowmeasinst.2021.101916
]Search in Google Scholar
[
Olyaie, E., Banejad, H., Heydari, M., 2019. Estimating discharge coefficient of PK-weir under subcritical conditions based on high-accuracy machine learning approaches. Iran. J. Sci. Technol. Trans. Civ. Eng., 43, 1, 89–101. http://dx.doi.org/10.1007/s40996-018-0150-z
]Search in Google Scholar
[
Parsaie, A., Haghiabi, A., 2015. The effect of predicting discharge coefficient by neural network on increasing the numerical modeling accuracy of flow over side weir. Water Resour. Manage., 29, 4, 973–985. http://dx.doi.org/10.1007/s11269-014-0827-4
]Search in Google Scholar
[
Parsaie, A., Haghiabi, A.H., 2021. Uncertainty analysis of discharge coefficient of circular crested weirs. Appl. Water Sci., 11, 2, 1–6. http://dx.doi.org/10.1007/s13201-020-01329-6
]Search in Google Scholar
[
Pospíšilík, Š., Zachoval, Z., 2023. Discharge coefficient, effective head and limit head in the Kindsvater-Shen formula for small discharges measured by thin-plate weirs with a triangular notch. J. Hydrol. Hydromech., 71, 1, 35–48. http://dx.doi.org/doi:10.2478/johh-2022-0040
]Search in Google Scholar
[
Prayogo, D., Susanto, Y.T.T., 2018. Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv. Civ. Eng., 2018, 1–9. http://dx.doi.org/10.1155/2018/6490169
]Search in Google Scholar
[
Ranga Raju Kittur, G., Gupta Sushil, K., Prasad, B., 1979. Side weir in rectangular channel. J. Hydraulics Div., 105, 5, 547–554. http://dx.doi.org/10.1061/JYCEAJ.0005207
]Search in Google Scholar
[
Říha, J., Zachoval, Z., 2014. Discharge coefficient of a trapezoidal broad-crested side weir for low approach Froude numbers. J. Hydraul. Eng., 140, 8, 1–6. http://dx.doi.org/10.1061/(ASCE)HY.1943-7900.0000889
]Search in Google Scholar
[
Říha, J., Zachoval, Z., 2015. Flow characteristics at trapezoidal broad-crested side weir. J. Hydrol. Hydromech., 63, 2, 164–171. http://dx.doi.org/10.1515/johh-2015-0026
]Search in Google Scholar
[
Roushangar, K., Akhgar, S., 2020. Particle swarm optimizationbased LS-SVM for hydraulic performance of stepped spillway. ISH J. Hydraul. Eng., 26, 3, 273–282. http://dx.doi.org/10.1080/09715010.2018.1481773
]Search in Google Scholar
[
Roy, M.-H., Larocque, D., 2019. Prediction intervals with random forests. Statistical Methods in Medical Research, 29, 1, 205–229. http://dx.doi.org/10.1177/0962280219829885
]Search in Google Scholar
[
Salmasi, F., Nouri, M., Sihag, P., Abraham, J., 2021. Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data. Water Supply, 21, 1, 232–248. http://dx.doi.org/10.2166/ws.2020.226
]Search in Google Scholar
[
Schulz, E., Speekenbrink, M., Krause, A., 2018. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. J. Math. Psychol., 85, 1–16. http://dx.doi.org/10.1016/j.jmp.2018.03.001
]Search in Google Scholar
[
Seyedian, S.M., Ghazizadeh, M.J., Tareghian, R., 2014. Determining side-weir discharge coefficient using Anfis. Proc. Inst. Civ. Eng. Water Manage., 167, 4, 230–237. http://dx.doi.org/10.1680/wama.12.00102
]Search in Google Scholar
[
Seyedian, S.M., Rouhani, H., 2015. Assessing ANFIS accuracy in estimation of suspended sediments. Građevinar, 67, 12, 1165–1176. http://dx.doi.org/10.14256/JCE.1210.2015
]Search in Google Scholar
[
Subramanya, K., Awasthy, S.C., 1972. Spatially varied flow over side-weirs. J. Hydraulics Div., 98, 1, 1–10. http://dx.doi.org/10.1061/JYCEAJ.0003188
]Search in Google Scholar
[
Suykens, J.A.K., De Brabanter, J., Lukas, L., Vandewalle, J., 2002. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48, 1, 85–105. http://dx.doi.org/10.1016/S0925-2312(01)00644-0
]Search in Google Scholar
[
Suykens, J.A.K., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Process. Lett., 9, 3, 293–300. http://dx.doi.org/10.1023/A:1018628609742
]Search in Google Scholar
[
Tao, H., Jamei, M., Ahmadianfar, I., Khedher, K.M., Farooque, A.A., Yaseen, Z.M., 2022. Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios. Eng. Appl. Comput. Fluid Mech., 16, 1, 1–19. http://dx.doi.org/10.1080/19942060.2021.2002721
]Search in Google Scholar
[
Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106, D7, 7183–7192. http://dx.doi.org/10.1029/2000JD900719
]Search in Google Scholar
[
Williams, C.K., Rasmussen, C.E., 2006. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA.
]Search in Google Scholar
[
Xiong, L., Wan, M., Wei, X., O’Connor, K.M., 2009. Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation. Hydrol. Sci. J., 54, 5, 852–871. http://dx.doi.org/10.1623/hysj.54.5.852
]Search in Google Scholar
[
Yadav, A., Hasan, M.K., Joshi, D., Kumar, V., Aman, A.H., Alhumyani, H., Alzaidi, M.S., Mishra, H., 2022. Optimized scenario for estimating suspended sediment yield using an artificial neural network coupled with a genetic algorithm. Water, 14, 18. http://dx.doi.org/10.3390/w14182815
]Search in Google Scholar
[
Yi, T., Zheng, H., Tian, Y., Liu, J.-P., 2018. Intelligent prediction of transmission line project cost based on least squares support vector machine optimized by particle swarm optimization. Math. Probl. Eng., 2018, 1–12. http://dx.doi.org/10.1155/2018/5458696
]Search in Google Scholar
[
Zhao, K., Popescu, S., Meng, X., Pang, Y., Agca, M., 2011. Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment, 115, 8, 1978–1996. http://dx.doi.org/10.1016/j.rse.2011.04.001
]Search in Google Scholar
[
Zounemat-Kermani, M., Golestani Kermani, S., Kiyaninejad, M., Kisi, O., 2019. Evaluating the application of data-driven intelligent methods to estimate discharge over triangular arced labyrinth weir. Flow Meas. Instrum., 68, 101573. http://dx.doi.org/10.1016/j.flowmeasinst.2019.101573
]Search in Google Scholar