Study for Filling Missing Wave Data in Geomundo Ocean Buoy Using Artificial Neural Networks
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study aimed to propose an Artificial neural network (ANN) model to fill missing wave data using Bayesian optimization of hyperparameters. Ocean environmental data obtained by ocean buoys have been missed due to the malfunction or maintenance of monitoring system or extremely harsh weather condition during a storm. It is important of the continuity of measured data to analyze ocean environmental condition for the engineering purpose such as the design condition for offshore structure and the assessment of wave condition for a long term return period using the extreme analysis. Five ANN models were applied to estimate three wave parameters of significant wave height, peak wave period, and wave direction using of measurement data at Geomundo ocean buoy for eight years (2010–2017). The wind data of European Centre for Medium-Range Weather Forecasts were employed to estimate the wave parameters with ANN models to fill missing wave data at Geomundo ocean buoy. By comparison of each ANN model result, it could be suggested Bidirectional gated recurrent unit network, Gated recurrent unit network, Feed-forward neural network for the best model to fill the significant wave height, peak wave period and wave direction, respectively. These three ANN models could be applied to fill a long-term missing wave data at ocean buoys.
Abstract
This study aimed to propose an Artificial neural network (ANN) model to fill missing wave data using Bayesian optimization of hyperparameters. Ocean environmental data obtained by ocean buoys have been missed due to the malfunction or maintenance of monitoring system or extremely harsh weather condition during a storm. It is important of the continuity of measured data to analyze ocean environmental condition for the engineering purpose such as the design condition for offshore structure and the assessment of wave condition for a long term return period using the extreme analysis. Five ANN models were applied to estimate three wave parameters of significant wave height, peak wave period, and wave direction using of measurement data at Geomundo ocean buoy for eight years (2010–2017). The wind data of European Centre for Medium-Range Weather Forecasts were employed to estimate the wave parameters with ANN models to fill missing wave data at Geomundo ocean buoy. By comparison of each ANN model result, it could be suggested Bidirectional gated recurrent unit network, Gated recurrent unit network, Feed-forward neural network for the best model to fill the significant wave height, peak wave period and wave direction, respectively. These three ANN models could be applied to fill a long-term missing wave data at ocean buoys.
참고문헌 (20)
- Alizadeh, M. J., "Multivariate GRU and LSTM models for wave forecasting and hindcasting in the southern Caspian Sea", Ocean Engineering, 298, pp.117193~ (2024)[DOI]
- Design and analysis of stationkeeping system for floating structure (API RP-2SK), "American Petroleum Institute(API)", ~ (2005)
- Standardized wave and wind environments for NATO operational areas, "Bales, S. L.", David W. Taylor Naval Ship Research and Development Center, ~ (1981)
- Bujak, D., "Filling missing and extending significant wave height measurements using neural networks and an integrated surface database", Wind, 3(2), pp.151~169 (2023)[DOI]
- Cao, W., "Brits : Bidirectional recurrent imputation for time series", Advances in neural information processing systems, 31 (2018)
- Cho, H. Y., "Parameter estimation and fitting error analysis of the representative spectrums using the wave spectrum off the Namhangjin, East Sea", Journal of Korean Society of Coastal and Ocean Engineers, 32(5), pp.363~371 (2020)[DOI]
- "Empirical evaluation of gated recurrent neural networks on sequence modeling/arXiv:1412.3555." (2014)[URL]
- Det Norske Veritas(DNV), "Recommend Practice (DNVRP-C205)", DNV, ~ (2014)
- Du, W., "Saits : Self-attention-based imputation for time series", Expert Systems with Applications, 219, pp.119619~ (2023)[DOI]
- European Centre for Medium-Range Weather Forecasts(ECMWF), "IFS documentation CY41R2", ~ (2016)
- Guijo-Rubio, D., "An evolutionary artificial neural network approach for spatio-temporal wave height time series reconstruction", Applied Soft Computing, 146, pp.110647~ (2023)[DOI]
- Hersbach, H., "The ERA5 global reanalysis", Quarterly Journal of the Royal Meteorological Society, 146(730), pp.1999~2049 (2020)[DOI]
- Hochreiter, S., "Long short-term memory", Neural computation, 9(8), pp.1735~1780 (1997)
- Kim, E. D., "Technical trends of time-series data imputation", Electronics and Telecommunications Trends, 36(4), pp.145~153 (2021)[DOI]
- "Copernicus Monitoring Environment Marine Service (CMEMS)", 2019
- Minuzzi, F. C., "A deep learning approach to predict significant wave height using long short-term memory", Ocean Modelling, 181, pp.102151~ (2023)[DOI]
- Pang, J., "A novel multivariable hybrid model to improve short and long-term significant wave height prediction", Applied Energy, 351, pp.121813~ (2023)[DOI]
- Park, S. B., "Prediction of significant wave height in korea strait using machine learning", Journal of Ocean Engineering and Technology, 35(5), pp.336~346 (2021)[DOI]
- Wei, Z., "Forecasting wind waves in the US Atlantic Coast using an artificial neural network model : Towards an AI-based storm forecast system", Ocean Engineering, 237, pp.109646~ (2021)[DOI]
- World Meteorological Organization(WMO), "Part A—Alphanumeric Codes", Manual on Codes: International Codes, I, 1~ (2019)