Filling gaps in significant wave height time series records using bidirectional gated recurrent unit and cressman analysis

Aguilera, 2020, Estimating extremely large amounts of missing precipitation data, J. Hydroinform., 22, 578, 10.2166/hydro.2020.127

Alexandre, 2015, A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction, Ocean Model., 92, 115, 10.1016/j.ocemod.2015.06.010

Arena, 2004, The reconstruction of significant wave height time series by using a neural network approach, J. Offshore Mech. Arct. Eng., 126, 213, 10.1115/1.1782646

Balas, 2004, Predictions of missing wave data by recurrent neuronets, J. Waterw. Port. Coast. Ocean Eng., 130, 256, 10.1061/(ASCE)0733-950X(2004)130:5(256)

Baltazar, 2006, Study of cubic splines and Fourier series as interpolation techniques for filling in short periods of missing building energy use and weather data, ASME. J. Sol. Energy Eng., 128, 226, 10.1115/1.2189872

Bocquet, 2014, Introduction to the principles and methods of data assimilation in geosciences

Bouttier, 2002, Data Assimilation Concepts and Methods March 1999, 718, 59

Chen, 2020, Improving coastal ocean wave height forecasting during typhoons by using local meteorological and neighboring wave data in support vector regression models, J. Mar. Sci. Eng., 8, 149, 10.3390/jmse8030149

Cressman, 1959, An operational objective analysis system, Mon. Weather Rev., 87, 367, 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2

Dobricic, 2010, Impact of data assimilation of glider observations in the Ionian Sea (Eastern Mediterranean), Dyn. Atmos. Oceans, 50, 78, 10.1016/j.dynatmoce.2010.01.001

Feng, 2022, Prediction of significant wave height in offshore china based on the machine learning method, J. Mar. Sci. Eng., 10, 836, 10.3390/jmse10060836

Hidalgo, O.S., Borge, J.C.N., Cunha, C., Guedes Soares, C., 1995. Filling missing observations in time series of significant wave height. Proceedings of the 14th International Conference on Offshore Mechanics and Arctic Engineering, vol. 2, pp. 9–17.

Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735

Li, 2022, Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series, Ocean Eng., 248, 10.1016/j.oceaneng.2022.110689

Malekmohamadi, 2011, Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction, Ocean Eng., 38, 487, 10.1016/j.oceaneng.2010.11.020

Mital, 2020, Sequential imputation of missing spatio-temporal precipitation data using random forests, Front. Water, 2, 20, 10.3389/frwa.2020.00020

Peres, 2015, Significant wave height record extension by neural networks and reanalysis wind data, Ocean Model., 94, 128, 10.1016/j.ocemod.2015.08.002

Ustoorikar, 2008, Filling up gaps in wave data with genetic programming, Mar. Struct., 21, 177, 10.1016/j.marstruc.2007.12.001

Vieira, 2020, A methodology for data gap filling in wave records using artificial neural networks, Appl. Ocean Res., 98, 10.1016/j.apor.2020.102109

Wang, 2021, Forecasting of significant wave height based on gated recurrent unit network in the taiwan strait and its adjacent waters, Water, 13, 86, 10.3390/w13010086

Yang, 2021, A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast, Renew. Energy, 173, 531, 10.1016/j.renene.2021.04.010

Yoon, 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J. Hydrol., 396, 128, 10.1016/j.jhydrol.2010.11.002

Zheng, 2020, Purely satellite data–driven deep learning forecast of complicated tropical instability waves, Sci. Adv., 6, 10.1126/sciadv.aba1482