全球海洋浮游植物动态分析与机器学习和重新分析的遥感
Subhrangshu Adhikary1, Surya Prakash Tiwari2, Saikat Banerjee3
1Spiraldevs Automation Industries Pvt. Ltd., Raiganj, West Bengal, India.
PeerJ
|May 13, 2024
概括
人工智能使用监督回归模型准确预测全球植物浮游生物水平. 这种方法有助于了解海洋生态系统和氧气生产,补充目前的测量方法.
科学领域:
- 海洋生物学 海洋生物学
- 海洋学 海洋学 海洋学
- 人工智能的人工智能
背景情况:
- 浮游生物是重要的海洋生物,生产地球大部分的氧气,并形成海洋食物网的基础.
- 盐度和pH值等环境因素显著影响植物浮游生物的生长和分布.
- 人工智能的进步为分析复杂的环境数据提供了新的工具.
研究的目的:
- 开发一个人工智能驱动的系统来预测全球植物浮游生物水平.
- 评估监督机器学习回归技术对此任务的有效性.
- 提供一个工具,补充现有的现场海洋学测量.
主要方法:
- 利用监督回归算法:随机森林,额外的树木,包装和基于直方图的梯度增强回归器.
- 在科珀尼克斯全球海洋生物地质化学Hindcast数据集上训练模型.
- 应用技术重新分析数据,以预测浮游植物度.
主要成果:
- 在预测植物浮游生物水平方面达到高确定系数 (R2),高达0.96.
- 证明了所选机器学习模型的有效性.
- 在大规模植物浮游生物监测中发现了人工智能的潜力.
结论:
- 监督机器学习回归模型显示了全球植物浮游生物水平的强大预测能力.
- 开发的AI模型有可能用于操作监控.
- 这种方法为传统的海洋学数据收集提供了有价值的补充.
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