监督学习分类建模的应用,用于预测加利福尼亚州南部海湾内盆地沉积物的毒性:概念测试
Omar Saleh1, Francesca Nyega Otim2, Ochan Otim3
1Department of Humanities and Sciences, University of California - Los Angeles, Los Angeles, CA 90024, USA.
The Science of the total environment
|August 4, 2023
概括
机器学习准确地预测了海洋沉积物的毒性. 一个渐变增强分类器模型在南加州海湾实现了87%的准确性,帮助环境分析.
科学领域:
- 环境科学 环境科学
- 海洋生物学 海洋生物学
- 数据科学数据科学数据科学
背景情况:
- 岩沉积物的毒性对海洋生态系统和人类健康构成风险.
- 分析广泛的沉积物毒性数据对于理解这些风险至关重要.
- 机器学习 (ML) 为从大型数据集中提取见解提供了强大的工具.
研究的目的:
- 测试使用ML来预测南加州海湾 (SCB) 沉积物毒性的可行性.
- 开发和评估一个ML分类器用于沉积物毒性预测,使用全加利福尼亚的数据集.
主要方法:
- 在5437个样本的加利福尼亚海岸数据集上训练了12个ML分类器.
- 使用了梯度增强分类器 (GBC) 模型,结合了度,经度和水深.
- 在70%的数据上验证了模型性能,并在未见的SCB数据上进行了测试.
主要成果:
- 在使用训练数据预测沉积物毒性时,GBC模型实现了83%的准确性.
- 度被确定为GBC模型中最重要的预测变量.
- 当将GBC模型应用于新的,不熟悉的SCB数据时,GBC模型显示87%的准确性.
结论:
- 监督的ML,特别是GBC,可以有效地预测SCB中的谷底沉积物毒性.
- 这种方法有助于环境分析和对海洋生态系统的风险评估.
- ML模型为管理和减轻沉积物相关的环境危险提供了宝贵的工具.
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