Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
권호 표지
DOI QR코드

DOI QR Code

Review on Applications of Machine Learning in Coastal and Ocean Engineering

  • Kim, Taeyoon (Institute of Marine Industry, Gyeongsang National University) ;
  • Lee, Woo-Dong (Department of Ocean Civil Engineering, Gyeongsang National University)
  • Received : 2022.03.30
  • Accepted : 2022.05.26
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

Keywords

Acknowledgement

This study was conducted under the support of the Korea Research Foundation with funding from the government in 2022 (Ministry of Science and ICT) (No. NRF-2022R1C1C2004838).

References

  1. Balas, C.E., Koc, L., & Balas, L. (2004). Predictions of Missing Wave Data by Recurrent Neuronets. Journal of Waterway, Port, Coastal, and Ocean Engineering, 130(5), 256-265. https://doi.org/10.1061/(ASCE)0733-950X(2004)130:5(256)
  2. Boser, B.E., Guyon, I., & Vapnik, V.N. (1992). A Training Algorithm for Optimal Margin Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 5, 144-152. Pittsburgh, ACM. https://doi.org/10.1145/130385.130401
  3. Chen, J., Pillai, A.C., Johanning, L., & Ashton, I. (2021). Using Machine Learning to Derive Spatial Wave Data: A Case Study for a Marine Energy Site. Environmental Modelling & Software, 142, 105066. https://doi.org/10.1016/j.envsoft.2021.105066
  4. De Rouck, J., Van de Walle, B., & Geeraerts, J. (2004). Crest Level Assessment of Coastal Structures by Full Scale Monitoring, Neural Network Prediction and Hazard Analysis on Permissible Wave Overtoppingg - (CLASH). Proceedings of the EurOCEAN 2004 (European Conference on Marine Science & Ocean Technology), Galway, Ireland, EVK3-CT-2001-00058, 261-262.
  5. Dwarakish, G.S., Rakshith, S., & Natesan, U. (2013). Review on Applications of Neural Network in Coastal Engineering. Artificial Intelligent Systems and Machine Learning, 5(7), 324-331.
  6. Den Bieman, J.P., Wilms, J.M., Van den Boogaard, H.F.P., & Van Gent, M.R.A. (2020). Prediction of Mean Wave Overtopping Discharge Using Gradient Boosting Decision Trees. Water, 12(6), 1703. https://doi.org/10.3390/w12061703
  7. Den Bieman, J.P., Van Gent, M.R.A., & Van den Boogaard, H.F.P. (2021). Wave Overtopping Predictions Using an Advanced Machine Learning Technique. Coastal Engineering, 166, 103830. https://doi.org/10.1016/j.coastaleng.2020.103830
  8. Deo, M.C., & Naidu, C.S. (1999). Real Time Wave Forecasting Using Neural Networks. Ocean Engineering, 26(3), 191-203. https://doi.org/10.1016/S0029-8018(97)10025-7
  9. Deo, M.C., & Jagdale, S.S. (2003). Prediction of Breaking Waves with Neural Networks. Ocean Engineering, 30(9), 1163-1178. https://doi.org/10.1016/S0029-8018(02)00086-0
  10. Etemad-Shahidi, A., & Bonakdar, L. (2009). Design of rubble-mound breakwaters using M5 machine learning method. Applied Ocean Research, 31(3), 197-201. https://doi.org/10.1016/j.apor.2009.08.003
  11. Etemad-Shahidi, A., Shaeri, S., & Jafari, E. (2016). Prediction of wave overtopping at vertical structures. Coastal Engineering 109, 42-52. https://doi.org/10.1016/j.coastaleng.2015.12.001
  12. Pullen, T., Allsop, N.W.H., Bruce, T., Kortenhaus, A., Schuttrumpf, H., & van der Meer, J.W. (2007). European Manual for the Assessment of Wave Overtopping. Pullen, T., Allsop, N.W.H., Bruce, T., Kortenhaus, A., Schuttrumpf, H. & van der Meer, J.W.(Eds.), HR Wallingford.
  13. Van der Meer, J.W., Allsop, N.W.H., Bruce, T., De Rouck, J., Kortenhaus, A., Pullen, T., ... Zanuttigh, B. (2018). Manual on Wave Overtopping of Sea Defences and Related Structures: An Overtopping Manual Largely Based on European Research, but for Worldwide Application (2nd ed.). EurOtop. Retrieved from http://www.overtopping-manual.com/assets/downloads/EurOtop_II_2018_Final_version.pdf
  14. Formentin, S.M., Zanuttigh, B., & van der Meer, J.W. (2017). A Neural Network Tool for Predicting Wave Reflection, Overtopping and Transmission. Coastal Engineering Journal, 59(1), 1750006-1-1750006-31. https://doi.org/10.1142/S0578563417500061
  15. Gandomi, M., Moharram, D.P., Iman, V., & Mohammad, R.N. (2020). Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques. Remote Sensing, 12(11), 1856. https://doi.org/10.3390/rs12111856
  16. Goyal, R., Singh, K., & Hegde, A.V. (2014). Quarter Circular Breakwater: Prediction 3 of Transmission Using Multiple Regression 4 and Artificial Neural Network. Marine Technology Society Journal, 48(1).
  17. Goldstein, E.B., Coco, G., & Plant, N.G. (2019). A Review of Machine Learning Applications to Coastal Sediment Transport and Morphodynamics. Earth-Science Reviews, 194, 97-108. https://doi.org/10.1016/j.earscirev.2019.04.022
  18. Gracia, S., Olivito, J., Resano, J., Martin-del-Brio, B., Alfonso, M., & Alvarez, E. (2021). Improving Accuracy on Wave Height Estimation Through Machine Learning Techniques. Ocean Engineering, 236, 108699. https://doi.org/10.1016/j.oceaneng.2021.108699
  19. Granta, F., & Nunno, F.D. (2021). Artificial Intelligence Models for Prediction of the Tide Level in Venice. Stochastic Environmental Research and Risk Assessment, 35, 2537-2548. https://doi.org/10.1007/s00477-021-02018-9
  20. Hashemi, M.R., Ghadampour, Z., & Neill, S.P., (2010). Using an Artifi cial Neural Network to Model Seasonal Changes in Beach Profi les. Ocean Engineering, 37(14-15), 1345-1356. https://doi.org/10.1016/j.oceaneng.2010.07.004
  21. Hosseinzadeh, S., Etemad-Shahidi, A., & Koosheh, A. (2021). Prediction of Mean Wave Overtopping at Simple Sloped Breakwaters Using Kernel-based Methods. Journal of Hydroinformatics, 23(5), 1030-1049. https://doi.org/10.2166/hydro.2021.046
  22. James, S.C., Zhang, Y., & O'Donncha, F. (2018). A Machine Learning Framework to Forecast Wave Conditions. Coastal Engineering, 137, 1-10. https://doi.org/10.1016/j.coastaleng.2018.03.004
  23. Kang, D.H., & Oh, S.J. (2019). A Study of Machine Learning Model for Prediction of Swelling Waves Occurrence on East Sea. Journal of Korean Institute of Information Technology, 17(9), 11-17. https://doi.org/10.14801/jkiit.2019.17.9.11
  24. Kankal, M., & Yuksek, O. (2012). Artificial Neural Network Approach for Assessing Harbor Tranquility: The Case of Trabzon Yacht Harbor, Turkey. Applied Ocean Research, 38, 23-31. https://doi.org/10.1016/j.apor.2012.05.009
  25. Kim, D.H., & Park, W.S. (2005). Neural Network for Design and Reliability Analysis of Rubble Mound Breakwaters. Ocean Engineering, 32, 1332-1349. https://doi.org/10.1016/j.oceaneng.2004.11.008
  26. Kim, D.H., Kim, Y.J., Hur, D.S., Jeon, H.S., & Lee, C.H. (2010). Calculating Expected Damage of Breakwater Using Artificial Neural Network for Wave Height Calculation. Journal of Korean Society of Coastal and Ocean Engineers, 22(2), 126-132.
  27. Kim, H.I., & Kim, B.H. (2020). Analysis of Major Rainfall Factors Affecting Inundation Based on Observed Rainfall and Random Forest. Journal of the Korean Society of Hazard Mitigation, 20(6), 301-310. https://doi.org/10.9798/KOSHAM.2020.20.6.301
  28. Kim, T., Kwon, S., & Kwon, Y. (2021). Prediction of Wave Transmission Characteristics of Low-Crested Structures with Comprehensive Analysis of Machine Learning. Sensors, 21(24), 8192. https://doi.org/10.3390/s21248192
  29. Kim, Y.E., Lee, K.E., & Kim, G.S. (2020). Forecast of Drought Index Using Decision Tree Based Methods. Journal of the Korean Data & Information Science Society, 31(2), 273-288. https://doi.org/10.7465/jkdi.2020.31.2.273
  30. Koc, M.L., Balas, C.E., & Koc, D.I. (2016). Stability Assessment of Rubble-mound Breakwaters Using Genetic Programming. Ocean Engineering, 111, 8-12. https://doi.org/10.1016/j.oceaneng.2015.10.058
  31. Koo, M.H., Park, E.G., Jeong, J., Lee, H.M., Kim, H.G., Kwon, M.J., ... Jo, S.B. (2016). Applications of Gaussian Process Regression to Groundwater Quality Data. Journal of Soil and Groundwater Environment, 21(6), 67-79. https://doi.org/10.7857/JSGE.2016.21.6.067
  32. Kuntoji, G., Manu, R., & Subba, R. (2020). Prediction of Wave Transmission over Submerged Reef of Tandem Greakwater Using PSO-SVM and PSO-ANN Techniques. ISH Journal of Hydraulic Engineering, 26(3), 283-290. https://doi.org/10.1080/09715010.2018.1482796
  33. Lee, G.H., Kim, T.G., & Kim, D.S.(2020). Prediction of Wave Breaking Using Machine Learning Open Source Platform. Journal of Korean Society Coastal and Ocean Engineers, 32(4), 262-272. https://doi.org/10.9765/KSCOE.2020.32.4.262
  34. Lee, J.S., & Suh, K.D. (2020). Development of Stability Formulas for Rock Armor and Tetrapods Using Multigene Genetic Programming. Journal of Waterway, Port, Coastal, and Ocean Engineering, 146(1), 04019027. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000540
  35. Lee, S.B., & Suh, K.D. (2019). Development of Wave Overtopping Formulas for Inclined Seawalls using GMDH Algorithm. KSCE Journal of Civil Engineering, 23, 1899-1910. https://doi.org/10.1007/s12205-019-1298-1
  36. Lee, T.L. (2004). Back-propagation Neural Network for Long-term Tidal Prediction. Ocean Engineering, 31(2), 225-238. https://doi.org/10.1016/S0029-8018(03)00115-X
  37. Li, B., Yin, J., Zhang, A., & Zhang, Z. (2018). A Precise Tidal Level Prediction Method Using Improved Extreme Learning Machine with Sliding Data Window. In 2018 37th Chinese Control Conference (CCC), 1787-1792. http://doi.org/10.23919/ChiCC.2018.8482902
  38. Liu, Y., Esan, O. C., Pan, Z., & An, L. (2021). Machine Learning for Advanced Energy Materials. Energy and AI, 3, 100049. https://doi.org/10.1016/j.egyai.2021.100049
  39. Lopez, I., Aragones, L., Villacampa, Y., Serra, J.C., (2017). Neural Network for Determining the Characteristic Points of the Bars. Ocean Engineering, 136, 141-151. https://doi.org/10.1016/j.oceaneng.2017.03.033
  40. Mahjoobi, J., Etemad-Shahidi, A., & Kazeminezhad, M.H. (2008). Hindcasting of Wave Parameters Using Different Soft Computing Methods. Applied Ocean Research, 30(1), 28-36. https://doi.org/10.1016/j.apor.2008.03.002
  41. Montano, J., Coco, G., Antolinez, J.A.A., Beuzen, T., Bryan, K.R., Cagigal, L., ... Vos, K. (2020). Blind Testing of Shoreline Evolution Models. Scientific Report, 10, 2137. https://doi.org/10.1038/s41598-020-59018-y
  42. Na, Y.Y., Park, J.G., & Moon, I.C. (2017). Analysis of Approval Ratings of Presidential Candidates Using Multidimensional Gaussian Process and Time Series Text Data. Proceedings of the Korean Operations Research And Management Society, Yeosu, 1151-1156.
  43. Najafzadeh, M., Barani, G.-A., & Kermani, M.R.H. (2014). Estimation of Pipeline Scour Due to Waves by GMDH. Journal of Pipeline Systems Engineering and Practice, 5(3), 06014002. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000171
  44. Oh, J., & Suh, K.-D. (2018). Real-time Forecasting of Wave Heights Using EOF-wavelet-neural Network Hybrid Model. Ocean Engineering, 150, 48-59. https://doi.org/10.1016/j.oceaneng.2017.12.044
  45. Panizzo, A., & Briganti, R. (2007). Analysis of Wave Transmission Behind Low-crested Breakwaters Using Neural Networks. Coastal Engineering, 54(9), 643-656. https://doi.org/10.1016/j.coastaleng.2007.01.001
  46. Park, J.S., Ahn, K.M., Oh, C.Y., & Chang, Y.S. (2020). Estimation of Significant Wave Heights from X-Band Radar Using Artificial Neural Network. Journal of Korean Society Coastal and Ocean Engineers, 32(6), 561-568. https://doi.org/10.9765/KSCOE.2020.32.6.561
  47. Passarella, M., Goldstein, E.B., De Muro, S., Coco, G. (2018). The Use of Genetic Programming to Develop a Predictor of Swash Excursion on Sandy Beaches. Natural Hazards and Earth System Sciences, 18, 599-611. https://doi.org/10.5194/nhess-18-599-2018
  48. Rigos, A., Tsekouras, G.E., Chatzipavlis, A., & Velegrakis, A.F. (2016). Modeling Beach Rotation Using a Novel Legendre Polynomial Feedforward Neural Network Trained by Nonlinear Constrained Optimization. In L. Iliadis, I. Maglogiannis (Eds.), Artificial Intelligence Applications and Innovations. AIAI 2016. IFIP Advances in Information and Communication Technology, 475, 167-179.
  49. Salehi, H., & Burgueno, R. (2018). Emerging Artificial Intelligence Methods in Structural Engineering. Engineering Structures, 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084
  50. Shahabi, S., Khanjani, M., & Kermani, M.H. (2016). Significant Wave Height Forecasting Using GMDH Model. International Journal of Computer Applications, 133(6), 13-16. https://doi.org/10.5120/ijca2016908129
  51. Shamshirband, S., Mosavi, A., Rabczuk, T., Nabipour, N., & Chau, K.W. (2020). Prediction of Significant Wave Height; Comparison Between Nested Grid Numerical Model, and Machine Learning Models of Artificial Neural Networks, Extreme Learning and Support Vector Machines. Engineering Applications of Computational Fluid Mechanics, 14(1), 805-817. https://doi.org/10.1080/19942060.2020.1773932
  52. Van der Meer, J.W. (1988). Rock Slopes and Gravel Beaches under Wave Attack (Ph.D. thesis). Delft University of Technology, Delft Hydraulics Report, 396.
  53. Van Gent, M.R.A., Van den Boogaard, H.F.P., Pozueta, B., & Medina, J.R. (2007). Neural Network Modelling of Wave Overtopping at Coastal Structures. Coastal Engineering, 54(8), 586-593. https://doi.org/10.1016/j.coastaleng.2006.12.001
  54. Wilson, K.E., Adams, P.N., Hapke, C.J., Lentz, E.E., & Brenner, O. (2015). Application of Bayesian Networks to Hindcast Barrier Island Morphodynamics. Coastal Engineering, 102, 30-43. https://doi.org/10.1016/j.coastaleng.2015.04.006
  55. Yagci, O., Mercan, D.E., Gigizoglu, H.K., & Kabdasli, M.S. (2005). Artificial Intelligence Methods in Breakwater Damage Ratio Estimation. Ocean Engineering, 32(17-18), 2088-2106. https://doi.org/10.1016/j.oceaneng.2005.03.004
  56. Yoon, H.-D., Cox, D.T., & Kim, M. (2013). Prediction of Time-dependent Sediment Suspension in the Surf Zone Using Artificial Neural Network. Coastal Engineering, 71, 78-86. https://doi.org/10.1016/j.coastaleng.2012.08.005
  57. Zanuttigh, B., Formentin, S.M., & Van der Meer, J.W. (2014). Advances in Modelling Wave- structure Interaction Tthrough Artificial Neural Networks. Coastal Engineering Proceedings, 1(34), 693.
  58. Zanuttigh, B., Formentin, S.M., & Van der Meer, J.W. (2016). Prediction of Extreme and Tolerable Wave Overtopping Discharges Thorough an Advanced Neural Network. Ocean Engineering, 127, 7-22. https://doi.org/10.1016/j.oceaneng.2016.09.032