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使用机器学习模型预测印尼省级河流中的微塑料数量.

Aan Priyanto1, Dian Ahmad Hapidin2, Dhewa Edikresnha2

  • 1Research Group of Physics and Technology of Advanced Materials, Department of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung, Jawa Barat 40132, Indonesia; Doctoral Program of Physics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung, Jawa Barat 40132, Indonesia.

The Science of the total environment
|January 10, 2025
PubMed
概括

机器学习模型准确预测了印度尼西亚河流中的微塑料含量. 树算法表现最好,确定温度,GDP和人口密度是影响污染的关键因素.

关键词:
人类遗传因素 人类遗传因素环境因素 环境因素印度尼西亚 河流 河流机器学习是机器学习.微塑料是一种微塑料.预测建模的预测建模.

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 生态毒理学 生态毒理学

背景情况:

  • 微塑料污染是一个重要的全球环境和健康问题.
  • 淡水系统是微塑料运输的关键途径.
  • 需要有效的监测策略来管理微塑料污染.

研究的目的:

  • 评估用于预测印尼河流中的微塑料度的机器学习模型.
  • 确定驱动微塑料丰富的关键环境和人为因素.
  • 为减轻淡水微塑料污染提供数据驱动的见解.

主要方法:

  • 应用了多个机器学习算法:树,k-最近邻居 (kNN),随机森林 (RF),线性回归 (LR),支持矢量机器 (SVM) 和神经网络 (NN).
  • 利用印度尼西亚24个省份的环境和人为数据.
  • 使用确定系数 (R2) 和平均绝对百分比误差 (MAPE) 验证模型性能.

主要成果:

  • 树算法表现出卓越的预测性能,其中R2 = 0.838和MAPE = 0.242.2.
  • 年平均气温,人均GDP和人口密度被确定为微塑料度的重要预测因素.
  • 该研究证实了机器学习在分析复杂环境数据方面的有效性.

结论:

  • 机器学习,特别是树算法,为预测和监测淡水系统中的微塑料污染提供了强大的工具.
  • 了解社会经济和气候因素的影响对于有针对性的污染控制至关重要.
  • 这项研究支持为印尼河流及其他地区制定明智的环境管理策略.