机器学习和深度学习在环境生态和健康方面的进展和应用
Shixuan Cui1, Yuchen Gao2, Yizhou Huang3
1Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
Environmental pollution (Barking, Essex : 1987)
|August 11, 2023
概括
机器学习 (ML) 和深度学习 (DL) 为分析复杂的环境数据和评估生态健康风险提供了强大的工具. 本综述强调了它们在环境生态与健康 (EEH) 中的应用和未来潜力.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 环境数据的数量和复杂性正在迅速增长.
- 传统方法难以分析环境生态与健康 (EEH) 中的大型,复杂的数据集.
- 机器学习 (ML) 和深度学习 (DL) 提供先进的分析能力.
研究的目的:
- 审查ML和DL在EEH的最新进展和应用.
- 总结基本的ML和DL算法及其对EEH的相关性.
- 在EEH中确定ML和DL的当前挑战和未来前景.
主要方法:
- 在EEH中对ML和DL应用的文献综述.
- 核心ML和DL建模过程和算法的总结.
- 确定研究热点和新兴趋势.
主要成果:
- 在EEH中,ML和DL是用于数据分析 (例如特征提取,分类,预测) 的多功能工具.
- 主要应用包括环境恢复,污染物的命运,化学物质暴露风险评估和危害控制.
- 取得了重大进展,但在ML和DL实施方面仍然存在挑战.
结论:
- ML和DL正在彻底改变EEH的研究范式.
- 迫切需要将ML和DL集成为创新的环境分析.
- 进一步开发和应用ML和DL将增强EEH的研究和实践.
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