The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A machine learning framework to forecast wave conditions
Open accessAbstract
A~machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wave model, accurate representations of significant wave heights and period can be used to predict ocean conditions. A model of Monterey Bay was used as the example test site; it was forced by measured wave conditions, ocean-current nowcasts, and reported winds. These input data along with model outputs of spatially variable wave heights and characteristic period were aggregated into supervised learning training and test data sets, which were supplied to machine learning models. These machine learning models replicated wave heights with a root-mean-squared error of 9cm and correctly identify over 90% of the characteristic periods for the test-data sets. Impressively, transforming model inputs to outputs through matrix operations requires only a fraction (<1/1,000) of the computation time compared to forecasting with the physics-based model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
Dynamics and Modelling of Ocean Waves
Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., & Janssen, P. A. E. M. (1994) · Cambridge University Press eBooks · doi:10.1017/cbo9780511628955 · Cited by 1,986
Kriging Metamodeling in Simulation: A Review
Kleijnen, J. P. C. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.10.013 · Cited by 1,355
Review of surrogate modeling in water resources
Razavi, S., Tolson, B. A., & Burn, D. H. (2012) · Water Resources Research · doi:10.1029/2011wr011527 · Cited by 983
The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)
Hodur, R. M. (1997) · Monthly Weather Review · doi:10.1175/1520-0493(1997)125<1414:tnrlsc>2.0.co;2 · Cited by 906
No Unbiased Estimator of the Variance of K-Fold Cross-ValidationPreprint
Bengio, Y., & Grandvalet, Y. (2003) · RePEc: Research Papers in Economics · Cited by 883
A Semi-implicit Ocean Circulation Model Using a Generalized Topography-Following Coordinate System
Song, Y., & Haidvogel, D. (1994) · Journal of Computational Physics · doi:10.1006/jcph.1994.1189 · Cited by 839
Cited by
Allal, Z., Noura, H. N., Salman, O., & Chahine, K. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120392 · Cited by 130
Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences
Boukabara, S.-A., Krasnopolsky, V., Penny, S. G., Stewart, J. Q., McGovern, A., Hall, D., Ten Hoeve, J. E., Hickey, J., Allen Huang, H.-L., Williams, J. K., et al. (2021) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-20-0031.1 · Cited by 87
Metadata
- Authors
- James, S. C., Zhang, Y., & O'Donncha, F.
- Venue
- Coastal Engineering
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 137
- Pages
- 1-10
- ISSN
- 0378-3839 (ISSN-L)
- OpenAlex
- W2760244693
- Cited by
- 401
- References
- 20
- FWCI
- 17.56
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A machine learning framework to forecast wave conditions. https://app.cochat.ai/papers/10.1016/j.coastaleng.2018.03.004
- The paper
- James, S. C., Zhang, Y., & O'Donncha, F. (2018). A machine learning framework to forecast wave conditions. Coastal Engineering. https://doi.org/10.1016/j.coastaleng.2018.03.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
