@inproceedings{scholars11329,
             doi = {10.1109/AiDAS47888.2019.8970706},
           pages = {1--6},
           title = {Survey of Sea Wave Parameters Classification and Prediction using Machine Learning Models},
            note = {cited By 7; Conference of 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019 ; Conference Date: 19 September 2019; Conference Code:157266},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
            year = {2019},
         journal = {Proceedings - 2019 1st International Conference on Artificial Intelligence and Data Sciences, AiDAS 2019},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079353118&doi=10.1109\%2fAiDAS47888.2019.8970706&partnerID=40&md5=771e5927f59ccee36e63d7f736eaeda3},
            isbn = {9781728130415},
        keywords = {Artificial intelligence; Classification (of information); Forecasting; Natural gas transportation; Oils and fats; Petroleum transportation; Surface waters; Surface waves; Surveys, Machine classifications; Marine datasets; Peak Spectral Period; Sea waves; Significant wave height; Wave period, Water waves},
          author = {Umair, M. and Hashmani, M. A. and Hasan, M. H. B.},
        abstract = {Sea has always played a pivotal role in human life. It formulates the weather, provides transportation medium, food, natural resources like oil and gas, and much more. Countless commercial and industrial activities take place on the surface of the sea, thus understanding, classifying and predicting the sea surface wave is a topic of great interest. Many numerical models (NM) have been proposed to model the behavior of sea waves, however, they are complex and costly for site-specific studies. On the other hand, data-driven machine learning (ML) models have recently proved to be an effective solution for site-specific classification, real-time or near-future prediction problems. The ML approach utilizes marine datasets to train, test and validate the model. In this paper, we present a survey of ML studies on the topic of classification and prediction of sea wave parameters. We hope that this paper provides a holistic model-based view to new researchers and pave the path for future research. {\^A}{\copyright} 2019 IEEE.}
}