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一个新的深度学习模型用于预测海洋污染,以实现可持续的海洋管理

Michael Onyema Edeh¹, Surjeet Dalal², Musaed Alhussein³

¹Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria.

PeerJ. Computer science

|December 9, 2024

概括

这项研究使用人工智能 (AI) 和机器学习来检测和分类海洋塑料污染. 一个自动化系统在识别浮动塑料垃圾方面取得了98.38%的准确性,有助于海洋清理工作.

科学领域:

环境科学环境科学
计算机科学计算机科学
海洋生物学海洋生物学

背景情况:

气候变化加剧了环境污染,海洋塑料垃圾对水生生态系统和海洋健康构成重大威胁.
技术进步虽然有助于气候变化,但也为灾害管理和环境意识提供了解决方案.

研究的目的:

使用人工智能 (AI) 调查和量化海洋和海底塑料污染.
开发和验证人工智能模型,用于识别和分类海洋环境中的塑料废物.

主要方法:

使用了两个机器学习算法:两步聚类和完全卷积网络 (FCN).
在Kaggle的现场获得的塑料定位数据上训练模型.
开发了一种通过实验测试验证的自动浮动塑料检测系统.

主要成果:

与传统方法相比,人工智能模型显示出有希望的准确性和性能.
自动浮动塑料检测系统在识别塑料废物方面取得了高准确率,达到98.38%.
该研究成功地对海洋和海底塑料废物进行了分类.

结论:

人工智能,特别是机器学习,为自动检测海洋塑料污染提供了强大的工具.
这项技术可以显著提高对抗海洋塑料垃圾和保护海洋环境的努力.

关键词:

两个步骤的聚类.完全卷积网络的网络完全卷积.海洋污染海洋污染海上交通运输海上交通.这是一个Yolo模型模型.

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