The application of Machine Learning (ML) to hydrologic modeling is fledgling. Its applicability to capture the dependencies on watersheds to forecast better within a short period is fascinating. One of the key reasons to adopt ML algorithms over physics-based models is its computational efficiency advantage and flexibility to work with various data sets. The diverse applications, particularly in emergency response and expanding over a large scale, demand the hydrological model in a short time and make researchers adopt data-driven modeling approaches unhesitatingly. In this work, in the era of ML and deep learning (DL), how it can help to improve the overall run time of physics-based model and potential constraints that should be addressed while modeling. This paper covers the opportunities and challenges of adopting ML for hydrological modeling and subsequently how it can help to improve the simulation time of physics-based models and future works that should be addressed.

翻译：机器学习在水文建模中的应用尚处于起步阶段。其能够在短时间内捕捉流域依赖性以实现更优预测的能力令人瞩目。相较于基于物理过程的模型，采用机器学习算法的关键原因之一在于其计算效率优势及处理多样化数据集的灵活性。特别是在应急响应和大尺度扩展等多元化应用场景中，对水文模型的快速构建需求促使研究者果断采用数据驱动的建模方法。本文探讨在机器学习与深度学习时代，如何利用相关技术提升基于物理过程模型的整体运行时间，以及在建模过程中需要解决的关键约束条件。本文系统阐述了机器学习应用于水文建模的机遇与挑战，进一步分析其如何助力缩短基于物理过程模型的模拟时间，并指出未来研究中亟待解决的关键问题。