Estimation of Significant Wave Heights from X-Band Radar Using Artificial Neural Network

Park, Jaeseong; Ahn, Kyungmo; Oh, Chanyeong; Chang, Yeon S.

doi:10.9765/kscoe.2020.32.6.561

Cited by 7 publications

(13 citation statements)

References 7 publications

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Section: Resultsmentioning

confidence: 99%

“…Additionally, to verify the reliability of the acquired data, the method introduced in [ 10 ] was used for interpretation. Specifically, the ANN method from [ 10 ] was applied to our radar data, and the results were summarized in the ANN section of Table 6 and the second row from the bottom of Table 7 . In [ 10 ], SNR, peak wave period, and wind condition were used as input variables for constructing the ANN, and the activation functions for the hidden layer and output layer were set the same as in [ 10 ].…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, the ANN method from [ 10 ] was applied to our radar data, and the results were summarized in the ANN section of Table 6 and the second row from the bottom of Table 7 . In [ 10 ], SNR, peak wave period, and wind condition were used as input variables for constructing the ANN, and the activation functions for the hidden layer and output layer were set the same as in [ 10 ]. Using the method from [ 10 ], we achieved an average correlation coefficient of 0.9642, an average RMSE of 0.3967, an average MAE of 0.2910, and an average MSE of 0.1581.…”

Section: Resultsmentioning

confidence: 99%

“…There have been several studies utilizing neural networks to estimate wave heights with X-band radar images. [ 9 ] used a set of synthesized X-band radar images to train a convolutional neural network trained (CNN), achieving an RMSE of 0.39 m. [ 10 ] estimated significant wave heights using a multilayer perceptron with three features obtained from X-band radar images as input variables, achieving an RMSE of 0.22 m.…”

Section: Introductionmentioning

confidence: 99%

“…These improvements, however, had not been validated with large-sized datasets. [ 9 ] utilized the synthesized dataset, while [ 10 – 12 ] were validated with radar images of 9 days. [ 13 ] used RGB ocean images over 46 days, but the number of image sequences was limited to 3019.…”

Section: Introductionmentioning

confidence: 99%

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Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

Kwon,

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2023

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This research introduces a deep learning method for ocean wave height estimation utilizing a Convolutional Neural Network (CNN) based on the VGGNet. The model is trained on a dataset comprising buoy wave heights and radar images, both critical for marine engineering. The dataset features X-band radar images sourced from Sokcho, Republic of Korea, spanning from June 1, 2021, to August 13, 2021. This collection amounts to 72,180 three-dimensional images, gathered at intervals of approximately 1.43 seconds. The data collected was highly unbalanced in terms of wave heights, with images of lower wave heights being more common. To deal with data imbalances in the wave height datasets, we categorized the data into three groups based on wave heights and applied stratified random sampling at each level. This approach balances the data patches for each training iteration, reducing the risk of overfitting and promoting learning from diverse data. We also implemented a system to protect data in groups with fewer instances, ensuring fair representation across all categories. This study presents a deep learning regression model for predicting wave height values from radar images. The model extracts features from sequences of 64 radar images using three-dimensional convolutions for both temporal and spatial learning. Using three-dimensional convolutions, the model captures temporal features in radar image sequences and provides accurate wave height estimates with an RMSE of 0.3576 m. The study derived results using radar images under different wave height conditions for 74 days to ensure reliability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

Kwon,

Chang,

Yang

2023

PLoS ONE

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Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Sindi,

Kim,

Yang

et al. 2024

DCE

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Aging ships and offshore structures face harsh environmental and operational conditions in remote areas, leading to age-related damages such as corrosion wastage, fatigue cracking, and mechanical denting. These deteriorations, if left unattended, can escalate into catastrophic failures, causing casualties, property damage, and marine pollution. Hence, ensuring the safety and integrity of aging ships and offshore structures is paramount and achievable through innovative healthcare schemes. One such paradigm, digital healthcare engineering (DHE), initially introduced by the final coauthor, aims at providing lifetime healthcare for engineered structures, infrastructure, and individuals (e.g., seafarers) by harnessing advancements in digitalization and communication technologies. The DHE framework comprises five interconnected modules: on-site health parameter monitoring, data transmission to analytics centers, data analytics, simulation and visualization via digital twins, artificial intelligence-driven diagnosis and remedial planning using machine and deep learning, and predictive health condition analysis for future maintenance. This article surveys recent technological advancements pertinent to each DHE module, with a focus on its application to aging ships and offshore structures. The primary objectives include identifying cost-effective and accurate techniques to establish a DHE system for lifetime healthcare of aging ships and offshore structures—a project currently in progress by the authors.

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Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

Huang

Yang

Chen

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Estimation of Significant Wave Heights from X-Band Radar Using Artificial Neural Network

Cited by 7 publications

References 7 publications

Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

Significant wave height prediction from X-band marine radar images using deep learning with 3D convolutions

Advancing digital healthcare engineering for aging ships and offshore structures: an in-depth review and feasibility analysis

Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network

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