Progress and challenges of artificial intelligence wave forecasting

doi:10.3969/j.issn.1001-909X.2024.03.002

Abstract

Abstract:

Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.

Key words: artificial intelligence (AI), ocean wave forecasting, development course, numerical model, intelligent forecasting, data-driven, difficulties and challenges, future trend

CLC Number:

P731.33
P714

LU Yuting, GUO Wenkang, DING Jun, WANG Linfeng, LI Xiaohui, WANG Jiuke. Progress and challenges of artificial intelligence wave forecasting[J]. Journal of Marine Sciences, 2024, 42(3): 28-37.

Figures/Tables 1

References 76

[1]	管长龙. 我国海浪理论及预报研究的回顾与展望[J]. 青岛海洋大学学报, 2000, 30(4):549-556.
	GUAN C L. A review of history and prospect for study of sea wave theory and its forecast in China[J]. Journal of Ocean University of Qingdao, 2000, 30(4): 549-556.
[2]	YOUNG I R, RIBAL A. Multiplatform evaluation of global trends in wind speed and wave height[J]. Science, 2019, 364(6440): 548-552. DOI PMID
[3]	HEMER M A, WANG X L, WEISSE R, et al. Advancing wind-waves climate science[J]. Bulletin of the American Meteorological Society, 2012, 93(6): 791-796.
[4]	WANG X L, SWAIL V R, ZWIERS F W, et al. Detection of external influence on trends of atmospheric storminess and northern oceans wave heights[J]. Climate Dynamics, 2009, 32: 189-203.
[5]	YOUNG I R, ZIEGER S, BABANIN A V. Global trends in wind speed and wave height[J]. Science, 2011, 332(6028): 451-455. DOI PMID
[6]	YOUNG I R, VINOTH J, ZIEGER S, et al. Investigation of trends in extreme value wave height and wind speed[J]. Journal of Geophysical Research: Oceans, 2012, 117: C00J06.
[7]	MENTASCHI L, VOUSDOUKAS M I, VOUKOUVALAS E, et al. Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns[J]. Geophysical Research Letters, 2017, 44(5): 2416-2426.
[8]	OUMERACI H. Review and analysis of vertical breakwater failures-Lessons learned[J]. Coastal Engineering, 1994, 22(1/2): 3-29.
[9]	PULLEN T, ALLSOP N W H, BRUCE T, et al. EurOtop: Wave overtopping of sea defences and related structures: Assessment manual[M]. [S.l.]: Boyens Medien GmbH, 2007.
[10]	VANEM E, FAZERES-FERRADOSA T, ROSA-SANTOS P, et al. Statistical description and modelling of extreme ocean wave conditions[J]. Proceedings of the Institution of Civil Engineers-Maritime Engineering, 2019, 172(4): 124-132.
[11]	ADYTIA D, SAEPUDIN D, PUDJAPRASETYA S R, et al. A deep learning approach for wave forecasting based on a spatially correlated wind feature, with a case study in the Java Sea, Indonesia[J]. Fluids, 2022, 7(1): 39.
[12]	TÜR R. Maximum wave height hindcasting using ensemble linear-nonlinear models[J]. Theoretical and Applied Clima-tology, 2020, 141: 1151-1163.
[13]	屈远, 高志一, 蔡靖泽, 等. 数值模型和智能模型的海浪预报能力比较[J]. 海洋预报, 2022, 39(5):17-26.
	QU Y, GAO Z Y, CAI J Z, et al. Comparison of wave prediction ability between numerical model and AI model[J]. Marine Forecasts, 2022, 39(5): 17-26.
[14]	OWENS E H. Beaufort wind scale[M]//SCHWARTZ M. Beaches and coastal geology. New York: Springer, 1982: 161-162.
[15]	许富祥, 许林之. 海浪预报方法综述(一)[J]. 海洋预报, 1989(3):33-43.
	XU F X, XU L Z. A review of wave forecasting methods (I)[J]. Marine Forecasts, 1989 (3): 33-43.
[16]	SVERDRUP H U, MUNK W H. Wind, sea and swell: theory of relations for forecasting[M]. Washington, D.C: Hydrographic Office, 1947.
[17]	文圣常. 普遍风浪谱及其应用[J]. 山东海洋学院学报, 1960:15-43.
	WEN S C. Universal wind wave spectrum and its applications[J]. Journal of Shandong Ocean University, 1960: 15-43.
[18]	PIERSON W J, NEUMANN G, JAMES R. Practical methods for observing and forecasting ocean waves by means of wave spectra and statistics[J]. Hydrographic Office Publication, 1955, 603: 300.
[19]	文圣常. 我在海浪理论及应用领域的研究工作[J]. 中国科学院院刊, 1996(2):61.
	WEN S C. My Research work in the field of wave theory and applications[J]. Bulletin of the Chinese Academy of Sciences, 1996(2): 61.
[20]	汪炳祥. 新型风浪谱及其应用[J]. 海洋与湖沼, 1962(Z2):142-160.
	WANG B X. New type of wind wave spectrum and its applications[J]. Oceanologia et Limnologia Sinica, 1962(Z2): 142-160.
[21]	汪炳祥. 风浪谱的形式的再探讨[J]. 海洋与湖沼, 1964(1): 23-37.
	WANG B X. Reconsideration of the form of wave spectrum[J]. Oceanologia et Limnologia Sinica, 1964(1): 23-37.
[22]	WILSON B W. Numerical prediction of ocean waves in the North Atlantic for December, 1959[J]. Deutsche Hydro-grafische Zeitschrift, 1965, 18(3): 114-130.
[23]	BRETSCHNEIDER C. Wave variability and wave spectra for wind generated gravity waves[J]. Beach Erosion Board, 1959: 196.
[24]	TSYPLUKHIN V F, KHASKHACHIKH G D, SAMARIN V G. U.S. Army coastal engineering research center[J]. Hydro-technical Construction, 1971, 5(9): 860-863.
[25]	关孟儒. 台风波浪推算方法的探讨[J]. 华东水利学院学报, 1981(2):17-36.
	GUAN M R. A discussion on the calculation method of typhoon waves[J]. Journal of East China College of Hydraulic Engineering, 1981(2): 17-36.
[26]	SWAMP Group. Wave dynamics (Book reviews: Ocean wave modeling)[J]. Science, 1985, 229.DOI:10.1126/science.229.4711.377.
[27]	PHILLIPS O M. The equilibrium range in the spectrum of wind-generated waves[J]. Journal of Fluid Mechanics, 1958, 4(4): 426.
[28]	WAMDI Group. The WAM model—a third generation ocean wave prediction model[J]. Journal of Physical Oceanography, 1988, 18(12): 1775-1810.
[29]	BOOIJ N, RIS R C, HOLTHUIJSEN L H. A third-generation wave model for coastal regions: 1. Model description and validation[J]. Journal of Geophysical Research: Oceans, 1999, 104(C4): 7649-7666.
[30]	HASSELMANN S. The WAM wave model system[R]. Max Planck Institut für Meteorologie, 1987.
[31]	TOLMAN H L, BALASUBRAMANIYAN B, BURROUGHS L D, et al. Development and implementation of wind-generated ocean surface wave modelsat NCEP[J]. Weather and Forecasting, 2002, 17(2): 311-333.
[32]	MOSAVI A, OZTURK P, CHAU K W. Flood prediction using machine learning models: Literature review[J]. Water, 2018, 10(11): 1536.
[33]	SHAMSHIRBAND S, MOSAVI A, RABCZUK T, et al. 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[J]. Engineering Applications of Compu-tational Fluid Mechanics, 2020, 14(1): 805-817.
[34]	ZENG P. Artificial neural networks principle for finite element method[J]. Zeitschrift fur Angewandte Mathematik und Mechanik, 1996, 76(S5): 565-566.
[35]	FUNAHASHI K I, NAKAMURA Y. Approximation of dynamical systems by continuous time recurrent neural networks[J]. Neural Networks, 2008, 6(6): 801-806. DOI:10.1016/S0893-6080(05)80125-X.
[36]	MARCHI E, VESPERINI F, EYBEN F, et al. A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks[C]// International Conference on Acoustics, Speech and Signal Processing (ICASSP). South Brisbane, QLD, Australia. IEEE, 2015: 1996-2000.
[37]	CHEN K, TAO W B. Once for all: A two-flow convolutional neural network for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(12): 3377-3386.
[38]	ŠEVO I, AVRAMOVIĆ A. Convolutional neural network based automatic object detection on aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 740-744.
[39]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[Z/OL]. arXiv, 2017: 1706.03762. http://arxiv.org/abs/1706.03762.
[40]	LIN T, WANG Y, LIU X, et al. A survey of transformers[J]. AI Open, 2022, 3: 111-132.
[41]	SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2009, 20(1): 61-80. DOI PMID
[42]	ZHOU J, CUI G Q, HU S D, et al. Graph neural networks: A review of methods and applications[J]. AI Open, 2020, 1: 57-81.
[43]	HUANG H, ZENG Z N, YAO D Y, et al. Spatial-temporal ConvLSTM for vehicle driving intention prediction[J]. Tsinghua Science and Technology, 2022, 27(3): 599-609.
[44]	LIM X Y, GAN K B, ABD AZIZ N A. Deep ConvLSTM network with dataset resampling for upper body activity recognition using minimal number of IMU sensors[J]. Applied Sciences, 2021, 11(8): 3543.
[45]	PENG S Q, XIE L. Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting[J]. Ocean Modelling, 2006, 14(1/2): 1-18.
[46]	LIU X, WANG Q, MU M. Optimal initial error growth in the prediction of the Kuroshio large meander based on a high-resolution regional ocean model[J]. Advances in Atmospheric Sciences, 2018, 35(11): 1362-1371.
[47]	ZHANG Z X, LI C W, LI Y S, et al. Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting[J]. Journal of Hydroinformatics, 2006, 8(1): 65-76.
[48]	许立兵, 王安喜, 汪纯阳, 等. 基于机器学习的海洋环境预报订正方法研究[J]. 海洋通报, 2020, 39(6): 695-704.
	XU L B, WANG A X, WANG C Y, et al. Research on correction method of ocean environmental forecasting based on machine learning[J]. Marine Bulletin, 2020, 39(6): 695-704.
[49]	ELLENSON A, PEI Y L, WILSON G, et al. An application of a machine learning algorithm to determine and describe error patterns within wave model output[J]. Coastal Engi-neering, 2020, 157: 103595.
[50]	GRACIA S, OLIVITO J, RESANO J, et al. Improving accuracy on wave height estimation through machine learning techniques[J]. Ocean Engineering, 2021, 236: 108699.
[51]	ALI M, PRASAD R. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition[J]. Renewable and Sustainable Energy Reviews, 2019, 104: 281-295.
[52]	DEO M C, SRIDHAR NAIDU C. Real time wave forecasting using neural networks[J]. Ocean Engineering, 1998, 26(3): 191-203.
[53]	LONDHE S N, PANCHANG V. One-day wave forecasts based on artificial neural networks[J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(11): 1593-1603.
[54]	DEKA P C, PRAHLADA R. Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time[J]. Ocean Engineering, 2012, 43: 32-42.
[55]	DIXIT P, LONDHE S, DANDAWATE Y. Removing predic-tion lag in wave height forecasting using Neuro-Wavelet modeling technique[J]. Ocean Engineering, 2015, 93: 74-83.
[56]	MAHJOOBI J, ADELI MOSABBEB E. Prediction of signi-ficant wave height using regressive support vector machines[J]. Ocean Engineering, 2009, 36(5): 339-347.
[57]	ELBISY M S. Sea wave parameters prediction by support vector machine using a genetic algorithm[J]. Journal of Coastal Research, 2015, 314: 892-899.
[58]	GOLESTANI M, ZEINODDINI M. Gap-filling and predicting wave parameters using support vector regression method[C]//Proceedings of ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering, Rotterdam, The Netherlands. 2011: 613-620.
[59]	NITSURE S P, LONDHE S N, KHARE K C. Wave forecasts using wind information and genetic programming[J]. Ocean Engineering, 2012, 54: 61-69.
[60]	KUMAR N K, SAVITHA R, AL MAMUN A. Ocean wave height prediction using ensemble of extreme learning machine[J]. Neurocomputing, 2018, 277: 12-20.
[61]	HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: A new learning scheme of feedforward neural networks[C]// 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). Budapest, Hungary. DOI:10.1109/IJCNN.2004.1380068.
[62]	KALOOP M R, KUMAR D, ZARZOURA F, et al. A wavelet-Particle swarm optimization-Extreme learning machine hybrid modeling for significant wave height prediction[J]. Ocean Engineering, 2020, 213: 107777.
[63]	MANDAL S, PRABAHARAN N. Ocean wave forecasting using recurrent neural networks[J]. Ocean Engineering, 2006, 33(10): 1401-1410.
[64]	WEI C C, CHENG J Y. Nearshore two-step typhoon wind-wave prediction using deep recurrent neural networks[J]. Journal of Hydroinformatics, 2020, 22(2): 346-367.
[65]	MIKY Y, KALOOP M R, ELNABWY M T, et al. A Recurrent-Cascade-Neural network-nonlinear autoregressive networks with exogenous inputs (NARX) approach for long-term time-series prediction of wave height based on wave characteristics measurements[J]. Ocean Engineering, 2021, 240: 109958.
[66]	FAN S T, XIAO N H, DONG S. A novel model to predict significant wave height based on long short-term memory network[J]. Ocean Engineering, 2020, 205: 107298.
[67]	JÖRGES C, BERKENBRINK C, STUMPE B. Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks[J]. Ocean Engineering, 2021, 232: 109046.
[68]	ZHOU S Y, BETHEL B J, SUN W J, et al. Improving significant wave height forecasts using a joint empirical mode decomposition—long short-term memory network[J]. Journal of Marine Science and Engineering, 2021, 9(7): 744.
[69]	ALI M, PRASAD R, XIANG Y, et al. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia[J]. Renewable Energy, 2021, 177: 1031-1044.
[70]	CHEN D L, LIU F, ZHANG Z Q, et al. Significant wave height prediction based on Wavelet Graph Neural Network[C]//2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI). Qingdao, China. IEEE, 2021: 80-85.
[71]	ZHOU S Y, XIE W H, LU Y X, et al. ConvLSTM-based wave forecasts in the South and East China Seas[J]. Frontiers in Marine Science, 2021, 8: 680079.
[72]	BAI G, WANG Z F, ZHU X Y, et al. Development of a 2-D deep learning regional wave field forecast model based on convolutional neural network and the application in South China Sea[J]. Applied Ocean Research, 2022, 118: 103012.
[73]	HOLTHUIJSEN L H. Waves in oceanic and coastal waters[M]. Cambridge, UK: Cambridge University Press, 2007.
[74]	张新宇, 石爱国, 周波, 等. 一种海浪谱预报的新模式[J]. 中国航海, 2015, 38(2):118-121.
	ZHANG X Y, SHI A G, ZHOU B, et al. A new method to predict ocean wave spectra[J]. Navigation of China, 2015, 38(2): 118-121.
[75]	MOONEYHAM J, CROSBY S C, KUMAR N, et al. SWRL net: A spectral, residual deep learning model for improving short-term wave forecasts[J]. Weather and Forecasting, 2020, 35(6): 2445-2460.
[76]	SONG Y H, JIANG H Y. A deep learning-based approach for empirical modeling of single-point wave spectra in open oceans[J]. Journal of Physical Oceanography, 2023, 53(9): 2089-2103.