因果机器学习方法用于理解土地使用和土地覆盖面的变化
F Eigenbrod1, Peter Alexander2, Nicolas Apfel3
1School of Geography and Environmental Science, University of Southampton, Southampton, SSO17 1BJ UK.
概括
因果机器学习 (ML) 方法为了解土地利用和土地覆盖变化 (LULCC) 驱动因素提供了一个有希望的方法. 将这些先进的方法与领域专业知识相结合,是有效制定LULCC政策的关键.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 由于复杂的社会生态过程,了解土地利用和土地覆盖变化 (LULCC) 驱动因素是一个关键的研究挑战.
- 为有效制定LULCC政策,因果模型是必不可少的,但目前的方法面临困难.
- LULCC社区对因果机器学习 (ML) 方法的理解和应用有限.
研究的目的:
- 为LULCC研究引入因果ML方法的最新技术.
- 概述因果ML在理解LULCC动态方面的局限性和潜在应用.
- 为了弥合因果ML进步和LULCC社区需求之间的差距.
主要方法:
- 举办了两次研讨会,以确定LULCC的有前途的ML方法.
- 专注于用于理解复杂LULCC动态的方法.
- 来自领域专家和ML从业人员的综合见解.
主要成果:
- 确定了与LULCC挑战相关的关键因果ML方法.
- 提供了与这些因果ML方法相关的局限性的概述.
- 用一个简单的例子来说明LULCC因果模型中的挑战.
结论:
- 因果ML方法显示了增强LULCC因果建模的显著潜力.
- 有效的政策设计需要将因果学习与深入的领域理解和定性见解相结合.
- 解决LULCC动态的复杂性对于成功应用因果ML至关重要.
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