废水中的机器学习:机遇和挑战 - - "不是一切都是钉子!"
Peter A Vanrolleghem1, Mostafa Khalil2, Marcello Serrao3
1modelEAU - Université Laval, Département de génie civil et de génie des eaux, Avenue de la Médecine, Québec, QC G1V 0A6, Canada.
Current opinion in biotechnology
|February 25, 2025
概括
机器学习 (ML) 为废水处理提供了潜力,但简单,可解释的模型至关重要. 建议环境工程师进行彻底的数据收集和基准模型,以提高ML可访问性.
科学领域:
- 环境工程 环境工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 为推进废水处理过程提供了重大机会.
- 在这个领域采用ML经常受到模型和数据挑战的复杂性所阻碍.
- 需要实用指导方针,以有效地在废水管理中实施ML.
研究的目的:
- 探索机器学习在废水处理中的潜在应用和关键考虑.
- 强调模型简单性,可解释性和ML应用中的可信度的重要性.
- 突出数据质量和元数据在ML模型的成功部署中的关键作用.
主要方法:
- 审查目前在废水处理中的机器学习应用.
- 分析模型复杂性和可解释性之间的权衡.
- 讨论数据要求,包括元数据收集和管理.
主要成果:
- 机器学习模型可以对废水应用非常有益,但必须仔细管理它们的复杂性.
- 过于复杂的"黑子"模型是不被鼓励的,特别是当数据有限时,由于解释性和信任的潜在问题.
- 高质量的数据收集和全面的元数据对于可靠的ML模型性能至关重要.
结论:
- 在废水应用的机器学习模型中,应优先考虑简单性和可解释性,以确保可信度.
- 解决数据稀缺问题以及建立数据和模型管理的最佳实践对于更广泛的ML采用至关重要.
- 未来的研究应该专注于开发基准混合模型,以提高环境工程师在有效利用ML工具方面的技能.
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