Improving accuracy on wave height estimation through machine learning techniques

Gracia, S.V. Juno Bella; Olivito, Javier; Resano, Javier; Martin-del-Brio, B.; Alfonso, Marcelo; Álvarez, Ernesto Navarro

doi:10.1016/j.oceaneng.2021.108699

Cited by 31 publications

(8 citation statements)

References 22 publications

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“…This might appeal to harbour-masters, fisherman, and others who depend on an accurate sea state forecast. Of course, the authors are aware of recent works in the forecast improvement area with Machine Learning algorithms (Guillou and Chapalain, 2021, Gracia et al, 2021, amongst many others). Readers interested in Machine Learning algorithms are referred to Ali et al (2021) for evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Improving the sea state forecasts by using local wave observations and the ensembleBMA software

Kokina,

Peláez-Zapata,

Murphy

et al. 2023

Environ. Data Science

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The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least $ 1\% $ , and in some cases up to $ 8\% $ . The reduction of error is between $ 6\% $ and $ 48\% $ . With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Improving the sea state forecasts by using local wave observations and the ensembleBMA software

Kokina,

Peláez-Zapata,

Murphy

et al. 2023

Environ. Data Science

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“…Normalization of data is the frst stage in designing a forecast using machine learning. It can facilitate the training process [27]. Tis data fall between 0 and 1.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

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In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.

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“…Machine learning (ML) is also a unique approach to predict wave heights. Various types of ML-based models can be utilized for real-time coastal and ocean environmental monitoring [17] and to improve the accuracy of wave height prediction and estimations [18,19].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the effectiveness of MEMS sensor and IoT in predicting wave height using machine learning models

Mishra

Singh

Chaudhary

2023

Meas. Sci. Technol.

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Wave height is a critical consideration in the planning and execution of maritime projects. Wave height forecasting methods include numerical and machine learning techniques. The traditional process involves using numerical wave prediction models, which have great success but are highly complex as they re-quire adequate information on nonlinear wind–wave and wave-wave interactions such as wave ener-gy-balance equation. On the contrary, machine learning techniques can predict wave height without prior knowledge of the above-mentioned complex interactions. This research aims to predict wave height using Micro-electromechanical Systems (MEMS), Internet of Things (IoTs), and Machine Learning (ML) based approaches. A floating buoy is developed using MEMS inertial measurement unit and IoT microcontrol-ler. An experiment is conducted in which the developed buoy is subjected to different wave heights in real time. The change in 3-Axis acceleration and 3-Axis gyroscope signals are acquired in a computer via IoT. These signals are analyzed using Machine learning-based classification models to predict wave height accurately. The obtained validation accuracy of the machine learning models K-NN (K-Nearest Neighbor), SVM (Support Vector Machine), and the bagged tree is 0.9906, 0.9368, and 0.9887 respec-tively, which indicates that MEMS and IoT can be used to classify & predict wave heights in real-time accurately.

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Improving accuracy on wave height estimation through machine learning techniques

Cited by 31 publications

References 22 publications

Improving the sea state forecasts by using local wave observations and the ensembleBMA software

Improving the sea state forecasts by using local wave observations and the ensembleBMA software

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Analyzing the effectiveness of MEMS sensor and IoT in predicting wave height using machine learning models

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