Abstract
Modeling of tsunami wave interaction with coral reefs to date focuses mainly on the process-based numerical models. In this study, an alternative machine learning technique based on the multi-layer perceptron neural network (MLP-NN) is introduced to predict the tsunami-like solitary wave run-up over fringing reefs. Two hydrodynamic forcings (incident wave height, reef-flat water level) and four reef morphologic features (reef width, fore-reef slope, beach slope, reef roughness) are selected as the input variables and wave run-up on the back-reef beach is assigned as the output variable. A validated numerical model based on the Boussinesq equations is applied to provide a dataset consisting of 4096 runs for MLP-NN training and testing. Results analyses show that the MLP-NN consisting of one hidden layer with ten hidden neurons provides the best predictions for the wave run-up. Subsequently, model performances in view of individual input variables are accessed via an analysis of the percentage errors of the predictions. Finally, a mean impact value analysis is also conducted to evaluate the relative importance of the input variables to the output variable. In general, the adopted MLP-NN has high predictive capability for wave run-up over the reef-lined coasts, and it is an alternative but more efficient tool for potential use in tsunami early warning system or risk assessment projects.
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References
Allawi MF, Aidan IA, El-Shafie A (2020) Enhancing the performance of data-driven models for monthly reservoir evaporation prediction. Environ Sci Pollut R. https://doi.org/10.1007/s11356-020-11062-x
Asma S, Sezer A, Ozdemir O (2012) MLR and ANN models of significant wave height on the west coast of India. Comput Geosci-UK 49(4):231–237. https://doi.org/10.1016/j.cageo.2012.05.032
Becker JM, Merrifield MA, Ford M (2014) Water level effects on breaking wave setup for Pacific island fringing reefs. J Geophys Res Oceans 119(2):914–932. https://doi.org/10.1002/2013JC009373
Callaghan DP, Baldock TE, Shabani B, Mumby PJ (2018) Communicating physics-based wave model predictions of coral reefs using bayesian belief networks. Environ Modell Softw 108:123–132. https://doi.org/10.1016/j.envsoft.2018.07.021
Chin RJ, Lai SH, Shaliza I, Wan Zurina WJ, Ahmed Elshafie AH (2019) New approach to mimic rheological actual shear rate under wall slip condition. Eng Comput 35:1409–1418. https://doi.org/10.1007/s00366-018-0670-y
Chin RJ, Lai SH, Ibrahim S, Jaafar WZW, Elshafie A (2020) ANFIS-based model for predicting actual shear rate associated with wall slip phenomenon. Soft Comput 24(13):9639–9649. https://doi.org/10.1007/s00500-019-04475-5
Chang HK, Liou JC, Liu SJ, Liaw SR (2011) Simulated wave-driven ANN model for typhoon waves. Adv Eng Softw 42(1–2):25–34. https://doi.org/10.1016/j.advengsoft.2010.10.014
Csáji BC (2001) Approximation with artificial neural networks. Dissertation, Eötvös Loránd University
Erdik T, Savci ME, Sen Z (2009) Artificial neural networks for predicting maximum wave runup on rubble mound structures. Expert Syst Appl 36(3):6403–6408. https://doi.org/10.1016/j.eswa.2008.07.049
Ford M, Becker JM, Merrifield MA, Song YT (2014) Marshall Islands fringing reef and atoll lagoon observations of the Tohoku Tsunami. Pure Appl Geophys 171(12):3351–3363. https://doi.org/10.1007/s00024-013-0757-8
Fernando HJS, Samarawickrama SP, Balasubramanian S, Hettiarachchi SSL, Voropayev S (2008) Effects of porous barriers such as coral reefs on coastal wave propagation. J Hydro-Environ Res 1(3–4):187–194. https://doi.org/10.1016/j.jher.2007.12.003
Gelfenbaum G, Apotsos A, Stevens AW, Jaffe B (2011) Effects of fringing reefs on tsunami inundation: american samoa. Earth-Sci Rev 107(1–2):12–22. https://doi.org/10.1016/j.earscirev.2010.12.005
Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci-UK 36(5):620–627. https://doi.org/10.1016/j.cageo.2009.09.014
Hagan MT, Menhaj M (1994) Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Networ 5(6):989–993. https://doi.org/10.1109/72.329697
Huang W, Murray C, Kraus N, Rosati J (2003) Development of a regional neural network for coastal water level predictions. Ocean Eng 30(17):2275–2295. https://doi.org/10.1016/S0029-8018(03)00083-0
Han G, Shi Y (2008) Development of an Atlantic Canadian coastal water level neural network model. J Atmos Ocean Tech 25(11):2117–2132. https://doi.org/10.1175/2008jtecho569.1
Harpham Q, Tozer N, Cleverley P, Wyncoll D, Cresswell D (2016) A Bayesian method for improving probabilistic wave forecasts by weighting ensemble members. Environ Modell Softw 84:482–493. https://doi.org/10.1016/j.envsoft.2016.07.015
Jiang J, Su X, Zhang H, Zhang X, Yuan Y (2013) A novel approach to active compounds identification based on support vector regression model and mean impact value. Chem Biol Drug Des 81(5):650–657. https://doi.org/10.1111/cbdd.12111
Kennedy B, Chen Q, Kirby JT, Dalrymple RA (2000) Boussinesq modeling of wave transformation, breaking, and run-up. I: 1D. J Waterway Port Coastal Ocean Eng 126(1):39–47. https://doi.org/10.1061/(ASCE)0733-950X(2000)126:1(39)
Kouvaras N, Dhanak MR (2018) Machine learning based prediction of wave breaking over a fringing reef. Ocean Eng 147(1):181–194. https://doi.org/10.1016/j.oceaneng.2017.10.005
Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Meth Prog Bio 113(1):202–209. https://doi.org/10.1016/j.cmpb.2013.10.011
Kunkel CM, Hallberg RW, Oppenheimer M (2006) Coral reefs reduce tsunami impact in model simulations. Geophys Res Lett 33(23):L23612. https://doi.org/10.1029/2006gl027892
López I, Aragonés L, Villacampa Y, Satorre R (2018) Modelling the cross-shore beach profiles of sandy beaches with Posidonia oceanica, using artificial neural networks: Murcia (Spain) as study case. Appl Ocean Res 74:205–216. https://doi.org/10.1016/j.apor.2018.03.004
Lynett P, Liu PLF (2002) Modeling wave generation, evolution, and interaction with depth-integrated. Cornell University Long and Intermediate Wave Modeling Package, Dispersive Wave Equations COULWAVE Code Manual
Liu PL-F, Lynett P, Fernando H, Jaffe BE, Fritz H, Higman B, Morton R, Goff J, Synolakis C (2005) Observations by the International Tsunami Survey Team in Sri Lanka. Science 308(5728):1595–1595. https://doi.org/10.1126/science.1110730
Mcadoo BG, Ah-Leong JS, Bell L, Ifopo P, Ward J, Lovell E, Skelton P (2011) Coral reefs as buffers during the 2009 south pacific tsunami, upolu island, samoa. Earth-Sci Rev 107(1–2):147–155. https://doi.org/10.1016/j.earscirev.2010.11.005
Marris E (2005) Tsunami damage was enhanced by coral theft. Nature 436(7054):1071–1071. https://doi.org/10.1038/4361071a
Nakama T (2011) Comparisons of single- and multiple-hidden-layer neural networks. In: Proceedings of the 8th international conference on Advances in neural networks - Part I, pp 270–279
Ning Y, Liu W, Sun Z, Zhao X, Zhang Y (2019) Parametric study of solitary wave propagation and runup over fringing reefs based on a Boussinesq wave model. J Mar Sci Technol 24:512–525. https://doi.org/10.1007/s00773-018-0571-1
Nwogu O (1993) Alternative form of Boussinesq equations for nearshore wave propagation. J Waterw Port Coast 119(6):618–638. https://doi.org/10.1061/(ASCE)0733-950X(1993)119:6(618)
Papadopoulos GA, Caputo R, McAdoo B, Pavlides S, Karastathis V, Fokaefs A, Orfanogiannaki K, Valkaniotis S (2006) The large tsunami of 26 December 2004: field observations and eyewitnesses accounts from Sri Lanka, Maldives Is. and Thailand. Earth Planets Space 58(2):233–241. https://doi.org/10.1186/BF03353383
Poelhekke L, Jäger WS, Van Dongeren A, Plomaritis TA, McCall R, Ferreira Ó (2016) Predicting coastal hazards for sandy coasts with a Bayesian network. Coastal Eng 118:21–34. https://doi.org/10.1016/j.coastaleng.2016.08.011
Pearson SG, Storlazzi CD, Dongeren ARV, Tissier MFS, Reniers AJHM (2017) A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts. J Geophys Res Oceans 122(12):10099–10117. https://doi.org/10.1002/2017JC013204
Plant NG, Thieler ER, Passeri DL (2016) Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network. Earths Future 4(5):143–158. https://doi.org/10.1002/2015EF000331
Quiroga PD, Cheung KF (2013) Laboratory study of solitary-wave transformation over bed-form roughness on fringing reefs. Coast Eng 80(4):35–48. https://doi.org/10.1016/j.coastaleng.2013.05.002
Roeber V (2010) Boussinesq-type mode for nearshore wave processes in fringing reef environment. PhD Thesis, University of Hawaii at Manoa
Roeber V, Cheung KF (2012) Boussinesq-type model for energetic breaking waves in fringing reef environments. Coast Eng 70(4):1–20. https://doi.org/10.1016/j.coastaleng.2012.06.001
Rodriguez-Delgado C, Bergillos RJ, Iglesias G (2019) An artificial neural network model of coastal erosion mitigation through wave farms. Environ Modell Softw 119:390–399. https://doi.org/10.1016/j.envsoft.2019.07.010
Wang W, Tang R, Li C, Liu P, Luo L (2018) A BP neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Eng 162:98–107. https://doi.org/10.1016/j.oceaneng.2018.04.039
Yao Y, Du R, Jiang C, Tang Z, Yuan W (2015) Experimental study of reduction of solitary wave run-up by emergent rigid vegetation on a beach. J Earthq Tsunami 9(5):1540003. https://doi.org/10.1142/S1793431115400035
Yao Y, He F, Tang Z, Liu Z (2018) A study of tsunami-like solitary wave transformation and run-up over fringing reefs. Ocean Eng 149:142–155. https://doi.org/10.1016/j.oceaneng.2017.12.020
Yao Y, He T, Deng Z, Chen L, Guo H (2019) Large eddy simulation modeling of tsunami-like solitary wave processes over fringing reefs. Nat Hazards Earth Syst Sci 19(6):1281–1295. https://doi.org/10.5194/nhess-19-1281-2019
Yan C, Zhang T, Sun Y, Tang H, Li H (2019) A hybrid variable selection method based on wavelet transform and mean impact value for calorific value determination of coal using laser-induced breakdown spectroscopy and kernel extreme learning machine. Spectrochim Acta B 154:75–81. https://doi.org/10.1016/j.sab.2019.02.007
Ye K, Zhang Y, Yang L, Zhao Y, Li N, Xie C (2018) Modeling convective heat transfer of supercritical carbon dioxide using an artificial neural network. Appl Therm Eng 150(5):686–695. https://doi.org/10.1016/j.applthermaleng.2018.11.031
Acknowledgements
This study was supported financially by the National Natural Science Foundation of China (Grant Nos. 51979013 and 51679014), the Scientific Research Fund of Hunan Provincial Education Department, China (Grant No. 18A116).
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Yao, Y., Yang, X., Lai, S.H. et al. Predicting tsunami-like solitary wave run-up over fringing reefs using the multi-layer perceptron neural network. Nat Hazards 107, 601–616 (2021). https://doi.org/10.1007/s11069-021-04597-w
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DOI: https://doi.org/10.1007/s11069-021-04597-w