Estimation and interpretation of equilibrium scour depth around circular bridge piers by using optimized XGBoost and SHAP

References

• Abd El-Hady Rady, R. (2020). Prediction of local scour around bridge piers: Artificial-intelligence-based modeling versus conventional regression methods. Applied Water Science, 10(2), 1–11. https://doi.org/10.1007/s13201-020-1140-4
   Web of Science ®Google Scholar
• Aksoy, A. O., Bombar, G., Arkis, T., & Guney, M. S. (2017). Study of the time-dependent clear water scour around circular bridge piers. Journal of Hydrology and Hydromechanics, 65(1), 26–34. https://doi.org/10.1515/johh-2016-0048
   Web of Science ®Google Scholar
• Aksoy, A. O., & Eski, O. Y. (2016). Experimental investigation of local scour around circular bridge piers under steady state flow conditions. Journal of the South African Institution of Civil Engineering, 58(3), 21–27. https://doi.org/10.17159/2309-8775/2016/v58n3a3
   Web of Science ®Google Scholar
• Alizamir, M., & Sobhanardakani, S. (2018). An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict heavy metals contamination in groundwater resources. Jundishapur Journal of Health Sciences, 10(2), 2. https://doi.org/10.5812/jjhs.67544
   Google Scholar
• Ansari, S. A., & Qadar, A. (1994). Ultimate depth of scour around bridge piers. Hydraulic Engineering, 51–55.
   Google Scholar
• Arneson, L. A., Zevenbergen, L. W., Lagasse, P. F., & Clopper, P. E. (2012). Evaluating scour at bridges. National Highway Institute (US).
   Google Scholar
• Bateni, S. M., Borghei, S. M., & Jeng, D. S. (2007a). Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 20(3), 401–414. https://doi.org/10.1016/j.engappai.2006.06.012
   Web of Science ®Google Scholar
• Bateni, S. M., Jeng, D. S., & Melville, B. W. (2007b). Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers. Advances in Engineering Software, 38(2), 102–111. https://doi.org/10.1016/j.advengsoft.2006.08.004
   Web of Science ®Google Scholar
• Blench, T. (1969). Mobile-bed fluviology. University of Alberta Press, Edmonton.
   Google Scholar
• Blench, T., Bradley, J. N., Joglekar, D. v., Bauer, W. J., Tison, L. J., Chitale, S. v., Thomas, A. R., Ahmad, M., & Romita, P. L. (1962). Discussion of “scour at bridge crossings.”. Transactions of the American Society of Civil Engineers, 127(1), 180–207. https://doi.org/10.1061/TACEAT.0008391
   Google Scholar
• Breusers, H. N. C. (1965). Scouring around drilling platforms. Bulletin, Hydraulic Research, IAHR, 19, 276.
   Google Scholar
• Breusers, H. N. C., Nicollet, G., & Shen, H. W. (1977). Local scour around cylindrical piers. Journal of Hydraulic Research, 15(3), 211–252. https://doi.org/10.1080/00221687709499645
   Web of Science ®Google Scholar
• Brownlee, J. (2020, August 14). Machine Learning Mastery. https://machinelearningmastery.com/.
   Google Scholar
• Chabert, J., & Engeldinger, P. (1956). Étude des affouillements autour des piles de ponts, Laboratoire National d’Hydraulique, Chatou, France.
   Google Scholar
• Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., & Chen, K. (2015). Xboost: Extreme gradient boosting. R Package Version 0.4-2, 1(4), 1–4.
   Google Scholar
• Chiew, Y. M. (1984). Local scour at bridge piers. Auckland University.
   Google Scholar
• Chitale, S. v. (1962). Discussion of scour at bridge crossing. Transactions of the American Society of Civil Engineers, 127(1), 191–196.
   Google Scholar
• Choi, S. U., Choi, B., & Lee, S. (2017). Prediction of local scour around bridge piers using the ANFIS method. Neural Computing and Applications, 28(2), 335–344. https://doi.org/10.1007/s00521-015-2062-1
   Web of Science ®Google Scholar
• Chou, J. S., & Nguyen, N. M. (2022). Scour depth prediction at bridge piers using metaheuristics-optimized stacking system. Automation in Construction, 140. https://doi.org/10.1016/j.autcon.2022.104297
   Web of Science ®Google Scholar
• Coleman, N. L. (1971). Analyzing laboratory measurements of scour at cylindrical piers in sand beds. Proc., 14th Congress I.A.H.R., International Association of Hydraulic Research.
   Google Scholar
• Cui, W., Zhao, L., Xu, Y., & Mamlooki, M. (2022). The compressive strength prediction for FRP-confined concrete in circular columns by applying the normalized AlexNet-ELM and the advanced red fox optimization algorithm. Advanced Theory and Simulations, 5(4), 2100410. https://doi.org/10.1002/adts.202100410
   Web of Science ®Google Scholar
• Dang, N. M., Tran Anh, D., & Dang, T. D. (2021). ANN optimized by PSO and firefly algorithms for predicting scour depths around bridge piers. Engineering with Computers, 37(1), 293–303. https://doi.org/10.1007/s00366-019-00824-y
   Web of Science ®Google Scholar
• Demir, S., & Sahin, E. K. (2023). Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost. Acta Geotechnica, https://doi.org/10.1007/s11440-022-01777-1
   PubMed Web of Science ®Google Scholar
• Dey, S., Bose, S. K., & Sastry, G. L. N. (1995). Clear water scour at circular piers: A model. Journal of Hydraulic Engineering, 121(12), 869–876. https://doi.org/10.1061/(ASCE)0733-9429(1995)121:12(869)
   Web of Science ®Google Scholar
• Dikshit, A., & Pradhan, B. (2021). Explainable AI in drought forecasting. Machine Learning with Applications, 6, 100192. https://doi.org/10.1016/j.mlwa.2021.100192
   Google Scholar
• Ebtehaj, I., Bonakdari, H., Zaji, A. H., & Sharafi, H. (2019). Sensitivity analysis of parameters affecting scour depth around bridge piers based on the non-tuned, rapid extreme learning machine method. Neural Computing and Applications, 31(12), 9145–9156. https://doi.org/10.1007/s00521-018-3696-6
   Web of Science ®Google Scholar
• Ebtehaj, I., Sattar Ahmed, M. A., Bonakdari, H., & Zaji, A. H. (2017). Prediction of scour depth around bridge piers using self-adaptive extreme learning machine. Journal of Hydroinformatics, 19(2), 207–224. https://doi.org/10.2166/hydro.2016.025
   Web of Science ®Google Scholar
• Einstein, A. (1916). Relativity: The special and general theory, 1920 translation edn. H. Holt and Company.
   Google Scholar
• Ettema, R. (1976). Influence of bed material gradation on local scour. University of Auckland.
   Google Scholar
• Ettema, R. (1980). Scour at bridge piers: Report No. 216. University of Auckland, School Of.
   Google Scholar
• Ettema, R., Kirkil, G., & Muste, M. (2006). Similitude of large-scale turbulence in experiments on local scour at cylinders. Journal of Hydraulic Engineering, 132(1), 33–40. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:1(33)
   Web of Science ®Google Scholar
• Fael, C., Lança, R., & Cardoso, A. (2016). Effect of pier shape and pier alignment on the equilibrium scour depth at single piers. International Journal of Sediment Research, 31(3), 244–250. https://doi.org/10.1016/j.ijsrc.2016.04.001
   Web of Science ®Google Scholar
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 1189–1232.
   Web of Science ®Google Scholar
• Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Froehlich, D. C. (1988). Analysis of onsite measurements of scour at piers. Hydraulic engineering: Proceedings of the 1988 national conference on hydraulic engineering, 534–539.
   Google Scholar
• Graf, W. H. (1995). Load scour around piers. Annual Report, Ecole Polytechnique Federal de Lausanne.
   Google Scholar
• Gu, K., Wang, J., Qian, H., & Su, X. (2021). Study on intelligent diagnosis of rotor fault causes with the PSO-XGBoost algorithm. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/9963146
   Web of Science ®Google Scholar
• Hancu, S. (1971). Sur le calcul des affouillements locaux dams la zone des piles des ponts. Proceedings of the 14th IAHR Congress, Paris, France, 3(1), 299–313.
   Google Scholar
• Hu, R., Wang, X., Liu, H., Leng, H., & Lu, Y. (2022). Scour characteristics and equilibrium scour depth prediction around a submarine piggyback pipeline. Journal of Marine Science and Engineering, 10(3), 350. https://doi.org/10.3390/jmse10030350
   Web of Science ®Google Scholar
• Inglis, S. C. (1949). Maximum depth of scour flatheads of guide bands and groynes, pier noses, and downstream bridges-the behavior and control of rivers and canals. Indian waterways experimental station, Poona, India, 327–348.
   Google Scholar
• Ishfaque, M., Salman, S., Jadoon, K. Z., Danish, A. A. K., Bangash, K. U., & Qianwei, D. (2022). Understanding the effect of hydro-climatological parameters on Dam seepage using shapley additive explanation (SHAP): A case study of earth-fill tarbela Dam, Pakistan. Water, 14(17), 2598. https://doi.org/10.3390/w14172598
   Web of Science ®Google Scholar
• Jain, S. C. (1981). Maximum clear-water scour around circular piers. Journal of the Hydraulics Division, 107(5), 611–626. https://doi.org/10.1061/JYCEAJ.0005667
   Web of Science ®Google Scholar
• Jain, S. C., & Fischer, E. E. (1979). Scour around bridge piers at high Froude numbers. FHWA-RD- 79-104, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C., U.S.A.
   Google Scholar
• Jalil, A., Faiz, R. b., Alyahya, S., & Maddeh, M. (2022). Impact of optimal feature selection using hybrid method for a multiclass problem in cross project defect prediction. Applied Sciences, 12(23), 12167. https://doi.org/10.3390/app122312167
   Google Scholar
• Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95-International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
   Google Scholar
• Khan, M., Tufail, M., Ajmal, M., Haq, Z. U., & Kim, T. W. (2017). Experimental analysis of the scour pattern modeling of scour depth around bridge piers. Arabian Journal for Science and Engineering, 42(9), 4111–4130. https://doi.org/10.1007/s13369-017-2599-7
   Web of Science ®Google Scholar
• Khattak, A., Chan, P. W., Chen, F., & Peng, H. (2022). Prediction and interpretation of low-level wind shear criticality based on Its altitude above runway level: Application of Bayesian optimization–ensemble learning classifiers and SHapley additive exPlanations. Atmosphere, 13(12), https://doi.org/10.3390/atmos13122102
   Web of Science ®Google Scholar
• Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E. T., Cordano, E., Endrizzi, S., & Kikinzon, E. (2017). The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 53(1), 867–890. https://doi.org/10.1002/2016WR019191
   Web of Science ®Google Scholar
• Kothyari, U. C., Garde, R. C. J., & Ranga Raju, K. G. (1992). Temporal variation of scour around circular bridge piers. Journal of Hydraulic Engineering, 118(8), 1091–1106. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:8(1091)
   Web of Science ®Google Scholar
• Kumar, A. (2007). Scour around circular compound bridge piers. Indian Institute of Technology Roorkee Roorkee.
   Google Scholar
• Kumar, A. (2011). Scour around circular piers founded in clay-sand-gravel sediment mixture. [Doctoral dissertation]. Indian Institute of Technology Roorkee.
   Google Scholar
• Lança, R. M., Fael, C. S., Maia, R. J., Pêgo, J. P., & Cardoso, A. H. (2013). Clear-water scour at comparatively large cylindrical piers. Journal of Hydraulic Engineering, 139(11), 1117–1125. https://doi.org/10.1061/(asce)hy.1943-7900.0000788
   Web of Science ®Google Scholar
• Landers, M. N., & Mueller, D. S. (1996). Channel scour at bridges in the United States.
   Google Scholar
• Larras, J. (1963). Profondeurs Maximales d’Erosion des Fonds Mobiles Autour des Piles en Rivere. Annales des Ponts et Chausses, 133, 411–424.
   Google Scholar
• Laursen, E. M., & Toch, A. (1956). Scour around bridge piers and abutments (Vol. 4). Iowa Highway Research Board Ames.
   Google Scholar
• Le, L. T., Nguyen, H., Zhou, J., Dou, J., & Moayedi, H. (2019). Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Applied Sciences, 9(13), 2714. https://doi.org/10.3390/app9132714
   Google Scholar
• Lee, S. O., & Sturm, T. W. (2009). Effect of sediment size scaling on physical modeling of bridge pier scour. Journal of Hydraulic Engineering, 135(10), 793–802. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000091
   Web of Science ®Google Scholar
• Lodhi, A. S., Jain, R. K., Sharma, P. K., & Karna, N. (2014). Time evolution of clear water bridge pier scour. Proceedings of international civil engineering symposium, VIT University Vellore, India, 252–260.
   Google Scholar
• López, G., Asce, F., Teixeira, L., Asce, F., Ortega-sánchez, M., Asce, F., Simarro, G., & Asce, F. (2014). Estimating final scour depth under clear-water flood waves. Journal of Hydraulic Engineering, 140(3), 328–332. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000804
   Web of Science ®Google Scholar
• Lucca, G., Junior, G. B., Cardoso, A., Paiva, D., Acatauassú, R., & Gattass, M. (2021). Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost. Expert Systems with Applications, 183(September 2020), https://doi.org/10.1016/j.eswa.2021.115452
   Google Scholar
• Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
   PubMedGoogle Scholar
• Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
   Google Scholar
• Lyn, D. A. (2008). Pressure-flow scour: A reexamination of the HEC-18 equation. Journal of Hydraulic Engineering, 134(7), 1015–1020.
   Web of Science ®Google Scholar
• Ma, M., Zhao, G., He, B., Li, Q., Dong, H., Wang, S., & Wang, Z. (2021). XGBoost-based method for flash flood risk assessment. Journal of Hydrology, 598, 126382. https://doi.org/10.1016/j.jhydrol.2021.126382
   Web of Science ®Google Scholar
• Mangalathu, S., Hwang, S. H., & Jeon, J. S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219. https://doi.org/10.1016/j.engstruct.2020.110927
   Web of Science ®Google Scholar
• Melville, B. W. (1997). Pier and abutment scour: Integrated approach. Journal of Hydraulic Engineering, 123(2), 125–136. https://doi.org/10.1061/(ASCE)0733-9429(1997)123:2(125)
   Web of Science ®Google Scholar
• Melville, B. W., & Chiew, Y.-M. (1999). Time scale for local scour at bridge piers. Journal of Hydraulic Engineering, 125(1), 59–65. https://doi.org/10.1061/(ASCE)0733-9429(1999)125:1(59)
   Web of Science ®Google Scholar
• Melville, B. W., & Coleman, S. E. (2000). Bridge scour. Water Resources Publication.
   Google Scholar
• Melville, B. W., & Sutherland, A. J. (1988). Design method for local scour at bridge piers. Journal of Hydraulic Engineering, 114(10), 1210–1226. https://doi.org/10.1061/(ASCE)0733-9429(1988)114:10(1210)
   Web of Science ®Google Scholar
• Mia, M. F., & Nago, H. (2003). Design method of time-dependent local scour at circular bridge pier. Journal of Hydraulic Engineering, 129(6), 420–427. https://doi.org/10.1061/(ASCE)0733-9429(2003)129:6(420)
   Web of Science ®Google Scholar
• Mueller, D. S., & Wagner, C. R. (2005). Field observations and evaluations of streambed scour at bridges. Federal Highway Administration. Office of Research.
   Google Scholar
• Najafzadeh, M., & Barani, G. A. (2011). Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Scientia Iranica, 18(6), 1207–1213. https://doi.org/10.1016/j.scient.2011.11.017
   Web of Science ®Google Scholar
• Najafzadeh, M., & Oliveto, G. (2020). Riprap incipient motion for overtopping flows with machine learning models. Journal of Hydroinformatics, 22(4), 749–767. https://doi.org/10.2166/hydro.2020.129
   Web of Science ®Google Scholar
• Natarajan, R., Megharaj, G., Marchewka, A., Divakarachari, P. B., & Hans, M. R. (2022). Energy and distance based multi-objective red fox optimization algorithm in wireless sensor network. Sensors, 22(10), 3761. https://doi.org/10.3390/s22103761
   PubMed Web of Science ®Google Scholar
• Neill, C. R. (1964). River-bed scour—a review for engineers: Ottawa, Canada, Canadian Good Roads Association Technical Publication No. 23.
   Google Scholar
• Neill, C. R. (1973). “Guide to bridge hydraulics.” Roads and Transportation Assoc. of Canada, University of Toronto Press, Toronto, Canada, 191pp.
   Google Scholar
• Nguyen, D. H., Le, X. H., Heo, J. Y., & Bae, D. H. (2021). Development of an extreme gradient boosting model integrated with evolutionary algorithms for hourly water level prediction. IEEE Access, 9, 125853–125867. https://doi.org/10.1109/ACCESS.2021.3111287
   Web of Science ®Google Scholar
• Ni, L., Wang, D., Wu, J., Wang, Y., Tao, Y., Zhang, J., & Liu, J. (2020). Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. Journal of Hydrology, 586, 124901. https://doi.org/10.1016/j.jhydrol.2020.124901
   Web of Science ®Google Scholar
• Oliveto, G., & Hager, W. H. (2002). Temporal evolution of clear-water pier and abutment scour. Journal of Hydraulic Engineering, 128(9), 811–820. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:9(811)
   Web of Science ®Google Scholar
• Osman, A. I. A., Ahmed, A. N., Chow, M. F., Huang, Y. F., & El-Shafie, A. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2), 1545–1556. https://doi.org/10.1016/j.asej.2020.11.011
   Web of Science ®Google Scholar
• Pandey, M., Oliveto, G., Pu, J. H., Sharma, P. K., & Ojha, C. S. P. (2020a). Pier scour prediction in non-uniform gravel beds. Water, 12(6), 1696. https://doi.org/10.3390/w12061696
   Web of Science ®Google Scholar
• Pandey, M., Sharma, P. K., Ahmad, Z., & Karna, N. (2018). Maximum scour depth around bridge pier in gravel bed streams. Natural Hazards, 91(2), 819–836. https://doi.org/10.1007/s11069-017-3157-z
   Web of Science ®Google Scholar
• Pandey, M., Zakwan, M., Khan, M. A., & Bhave, S. (2020b). Development of scour around a circular pier and its modelling using genetic algorithm. Water Supply, 20(8), 3358–3367. https://doi.org/10.2166/ws.2020.244
   Google Scholar
• Pham, B. T., Qi, C., Ho, L. S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M. D., Nguyen, H. D., Ly, H.-B., Le, H. v., & Prakash, I. (2020). A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil. Sustainability, 12(6), 2218. https://doi.org/10.3390/su12062218
   Web of Science ®Google Scholar
• Połap, D., & Woźniak, M. (2021). Red fox optimization algorithm. Expert Systems with Applications, 166(September 2020), 114107. https://doi.org/10.1016/j.eswa.2020.114107
   Google Scholar
• Qiu, Y., Zhou, J., Khandelwal, M., Yang, H., Yang, P., & Li, C. (2021). Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers, 0123456789. https://doi.org/10.1007/s00366-021-01393-9
   Web of Science ®Google Scholar
• Raikar, R. v., & Dey, S. (2005). Clear water scour at bridge piers in fine and medium gravel beds. Canadian Journal of Civil Engineering, 781(1980), 775–781. https://doi.org/10.1139/L05-022
   Google Scholar
• Richardson, E. v., & Davis, S. R. (2001). Evaluating scour at bridges. Federal Highway Administration. Office of Bridge Technology.
   Google Scholar
• Roder, M., de Rosa, G. H., Passos, L. A., Papa, J. P., & Rossi, A. L. D. (2020). Harnessing particle swarm optimization through relativistic velocity. 2020 IEEE congress on evolutionary computation, CEC 2020 - conference proceedings.
   Google Scholar
• Shamshirband, S., Mosavi, A., & Rabczuk, T. (2020). Particle swarm optimization model to predict scour depth around a bridge pier. Frontiers of Structural and Civil Engineering, 14(4), 855–866. https://doi.org/10.1007/s11709-020-0619-2
   Web of Science ®Google Scholar
• Shapley, L. S. (1953). A value for n-person games.
   Google Scholar
• Sharafi, H., Ebtehaj, I., Bonakdari, H., & Zaji, A. H. (2016). Design of a support vector machine with different kernel functions to predict scour depth around bridge piers. Natural Hazards, 84(3), 2145–2162. https://doi.org/10.1007/s11069-016-2540-5
   Web of Science ®Google Scholar
• Shen, H. W. (1971). Scour Near Piers. River Mechanics, vol. II. Ft. Collins, Colo (Chapter 23).
   Google Scholar
• Shen, H. W., Schneider, V. R., & Karaki, S. (1969). Local scour around bridge piers. Journal of the Hydraulics Division, 95(6), 1919–1940. https://doi.org/10.1061/JYCEAJ.0002197
   Google Scholar
• Sheppard, D. M. (2004). Overlooked local sediment scour mechanism. Transportation Research Record: Journal of the Transportation Research Board, 1890(1), 107–111. https://doi.org/10.3141/1890-13
   Google Scholar
• Sheppard, D. M., Melville, B., & Demir, H. (2010). SCOUR AT WIDE PIERS AND LONG SKEWED PIERS FINAL REPORT Prepared for NCHRP Transportation Research Board Of The National Academies.
   Google Scholar
• Sheppard, D. M., Melville, B., & Demir, H. (2014). Evaluation of existing equations for local scour at bridge piers. Journal of Hydraulic Engineering, 140(1), 14–23. https://doi.org/10.1061/(asce)hy.1943-7900.0000800
   Web of Science ®Google Scholar
• Shi, R., Xu, X., Li, J., & Li, Y. (2021). Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization. Applied Soft Computing, 109. https://doi.org/10.1016/j.asoc.2021.107538
   Web of Science ®Google Scholar
• Sreedhara, B. M., Patil, A. P., Pushparaj, J., Kuntoji, G., & Naganna, S. R. (2021). Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers. Journal of Hydroinformatics, 23(4), 849–863. https://doi.org/10.2166/hydro.2021.011
   Web of Science ®Google Scholar
• Sreedhara, B. M., Rao, M., & Mandal, S. (2019). Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers. Neural Computing and Applications, 31(11), 7335–7349. https://doi.org/10.1007/s00521-018-3570-6
   Web of Science ®Google Scholar
• Taormina, R., & Chau, K. (2015). Neural network river forecasting with multi-objective fully informed particle swarm optimization. Journal of Hydroinformatics, 17(1), 99–113. https://doi.org/10.2166/hydro.2014.116
   Web of Science ®Google Scholar
• Tavouktsoglou, N. S., Harris, J. M., Simons, R. R., & Whitehouse, R. J. S. (2017). Equilibrium scour-depth prediction around cylindrical structures. Journal of Waterway, Port, Coastal, and Ocean Engineering, 143(5), 04017017.
   Web of Science ®Google Scholar
• Venkatesan, E., & Mahindrakar, A. B. (2019). Forecasting floods using extreme gradient boosting-a new approach. International Journal of Civil Engineering and Technology, 10(2), 1336–1346.
   Google Scholar
• Wang, C., Yu, X., & Liang, F. (2017). A review of bridge scour: Mechanism, estimation, monitoring and countermeasures. Natural Hazards, 87(3), 1881–1906. https://doi.org/10.1007/s11069-017-2842-2
   Web of Science ®Google Scholar
• Wardhana, K., & Hadipriono, F. C. (2003). Analysis of recent bridge failures in the United States. Journal of Performance of Constructed Facilities, 17(3), 144–150. https://doi.org/10.1061/(ASCE)0887-3828(2003)17:3(144)
   Web of Science ®Google Scholar
• Williams, P., Bolisetti, T., & Balachandar, R. (2017). Evaluation of governing parameters on pier scour geometry. Canadian Journal of Civil Engineering, 44(1), 48–58. https://doi.org/10.1139/cjce-2016-0133
   Web of Science ®Google Scholar
• Wu, J., Ma, D., & Wang, W. (2022). Leakage identification in water distribution networks based on XGBoost algorithm. Journal of Water Resources Planning and Management, 148(3), 4021107. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001523
   Web of Science ®Google Scholar
• Yanmaz, A. M., & Altinbilek, H. D. g. ˇ. a. (1991). Study of time-depenbent local scour around bridge piers. Journal of Hydraulic Engineering, 117(10), 1247–1268. https://doi.org/10.1061/(ASCE)0733-9429(1991)117:10(1247)
   Web of Science ®Google Scholar
• Yu, J., Zheng, W., Xu, L., Zhangzhong, L., Zhang, G., & Shan, F. (2020). A pso-xgboost model for estimating daily reference evapotranspiration in the solar greenhouse. Intelligent Automation and Soft Computing, 26(5), 989–1003. https://doi.org/10.32604/iasc.2020.010130
   Web of Science ®Google Scholar
• Zhang, X., Nguyen, H., Bui, X., Tran, Q., Nguyen, D., Bui, D. T., & Moayedi, H. (2020). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research, 29(2), 711–721. https://doi.org/10.1007/s11053-019-09492-7
   Web of Science ®Google Scholar
• Zhang, Y., Lin, R., Zhang, H., & Peng, Y. (2022). Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization. Complex and Intelligent Systems, 9(1), 133–145.
   Web of Science ®Google Scholar
• Zhou, J., Qiu, Y., Zhu, S., & Jahed, D. (2021). Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space, 6(5), 506–515. https://doi.org/10.1016/j.undsp.2020.05.008
   Web of Science ®Google Scholar
• Zounemat-Kermani, M., Beheshti, A. A., Ataie-Ashtiani, B., & Sabbagh-Yazdi, S. R. (2009). Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied Soft Computing Journal, 9(2), 746–755. https://doi.org/10.1016/j.asoc.2008.09.006
   Web of Science ®Google Scholar