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先进的机器学习模型用于估计海洋表面颗粒有机碳 (POC) 度的分布,使用卫星遥感数据:地中海作为一个例子

Chao Li¹, Huisheng Wu¹, Chaojun Yang¹

¹College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China.

Sensors (Basel, Switzerland)

|September 14, 2024

概括

本研究确定了使用卫星数据估计地中海有机颗粒碳 (POC) 的最佳机器学习模型. tuneRanger随机森林模型实现了高精度,改善了我们对海洋碳循环的理解.

科学领域:

海洋学海洋学海洋学
海洋生物地质化学海洋生物地质化学
遥感遥感遥感遥感

背景情况:

准确估计海洋表面的微粒有机碳 (POC) 分布对于了解盆地规模的海洋碳循环至关重要.
卫星遥感为大规模的POC估计提供了可行的方法,但需要强大的机器学习方法来实现准确性.

研究的目的:

通过卫星数据确定最佳的机器学习模型,以估计地中海海面POC度.
开发和验证高精度的POC卫星检索方法,提高对其动态的理解.

主要方法:

利用地理探测器 (GD) 方法分析POC和47个环境因素之间的空间相关性.
我们比较了四种机器学习模型:贝叶斯优化随机森林 (BRF),反向传播神经网络,自适应增强和极端梯度增强.
优化了表现最好的模型,一个tuneRanger随机森林 (TRRF),使用tuneRanger R包.

主要成果:

贝叶斯优化随机森林 (BRF) 在初始模型中表现出优越的性能.
优化的tuneRanger随机森林 (TRRF) 模型实现了高精度,R2 = 0.868,MSE = 1.119 (mg/m3) 2和MAE = 1.041 mg/m3.
空间分析显示,西部和北部以及地中海沿海地区附近的POC度较高.

结论:

tuneRanger随机森林模型提供了一个高度准确的方法,用于卫星检索海面POC度.
这项研究增强了对地中海POC分布和动态的理解,同时考虑了环境因素和人类影响.

关键词:

地中海地中海地中海地中海地中海地中海地理探测器地理探测器机器学习是机器学习.有机碳颗粒有机碳颗粒在 tuneRanger R 软件包中.

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