Survey of Sea Wave Parameters Classification and Prediction using Machine Leaming Models

Muhammad Umair, M. Hashmani, M. H. Hasan
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引用次数: 5

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.
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基于机器学习模型的海浪参数分类与预测研究综述
海洋在人类生活中一直扮演着举足轻重的角色。它决定天气,提供运输媒介、食物、石油和天然气等自然资源等等。无数的商业和工业活动发生在海面上,因此了解、分类和预测海面波浪是一个非常有趣的话题。许多数值模型(NM)已被提出来模拟海浪的行为,然而,它们是复杂的和昂贵的特定地点的研究。另一方面,数据驱动的机器学习(ML)模型最近被证明是特定站点分类、实时或近期预测问题的有效解决方案。机器学习方法利用海洋数据集来训练、测试和验证模型。本文就海浪参数分类与预测这一主题的机器学习研究作一综述。我们希望本文能够为新研究者提供一个基于整体模型的视角,并为未来的研究铺平道路。
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