This paper presents an overview of scientific modeling and discusses the
complementary strengths and weaknesses of ML methods for scientific modeling in
comparison to process-based models. It also provides an introduction to the
current state of research in the emerging field of scientific knowledge-guided
machine learning (KGML) that aims to use both scientific knowledge and data in
ML frameworks to achieve better generalizability, scientific consistency, and
explainability of results. We discuss different facets of KGML research in
terms of the type of scientific knowledge used, the form of knowledge-ML
integration explored, and the method for incorporating scientific knowledge in
ML. We also discuss some of the common categories of use cases in environmental
sciences where KGML methods are being developed, using illustrative examples in
each category.

科学建模的综述及与基于过程的模型相比，机器学习方法在科学建模中的互补优势和不足；以及科学知识引导的机器学习（KGML）领域的研究现状介绍，旨在利用科学知识和数据在机器学习框架中实现更好的泛化性、科学一致性和可解释性的结果；我们讨论了 KGML 研究的不同方面：使用的科学知识类型、知识 - 机器学习集成的形式以及在机器学习中融入科学知识的方法；同时，我们还讨论了环境科学中 KGML 方法正在开发的常见应用类别，并提供了每个类别的例证。