以知识为导向的机器学习促进全球变革 生态学研究 研究生态学研究
Zhenong Jin1,2, Licheng Liu3, Qi Yang4
1Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing, China.
Global change biology
|February 10, 2026
概括
知识引导机器学习 (KGML) 将生态原则与人工智能集成,为全球变化生态创造更好的预测模型. 这种方法提高了对生态系统反应的理解,并支持可持续发展目标.
科学领域:
- 生态生态学 生态生态学
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 全球变化生态学需要预测模型,将数据驱动学习与机械理论结合起来.
- 传统模型在时空参数化 (基于过程) 或概括和可解释性 (数据驱动) 方面面临挑战.
- 现有的方法很难有效地应对复杂的,相互关联的生态系统挑战.
研究的目的:
- 审查知识引导机器学习 (KGML) 在全球变化生态中的变革潜力.
- 展示KGML提高对关键生态过程,如碳-水-营养循环的预测能力.
- 探索KGML在开发生态基础模型和获得可操作见解方面的作用.
主要方法:
- 在机器学习模型中系统地整合生态原理 (例如,物理定律,固体测量,过程理解).
- 设计,培训和调整模型以确保在多样化的生态系统中实现泛化.
- 审查决策支持和符号回归中的新兴应用.
主要成果:
- KGML提供了一个强大的框架,可以弥合数据驱动型和理论驱动型建模方法之间的差距.
- 证明了碳-水-营养循环和其他生态过程的增强预测能力.
- 突出了开发生态基础模型和产生新假设的潜力.
结论:
- KGML代表了全球变化生态学的重大进步,将生态理论与AI结合起来.
- 未来的方向包括自适应性数据知识集成,不确定性量化和因果嵌入.
- KGML对于促进科学发现和开发生态系统挑战的可持续解决方案至关重要.
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