数据科学家的流量预测指南
A Data Scientist's Guide to Streamflow Prediction
摘要：
近年来，数据驱动科学的范式已成为物理科学的主要成分，特别是在气候学等地球物理学科中。水文学领域是机器学习和数据驱动模型引起广泛关注的学科之一。这为数据科学家对水文研究的贡献提供了巨大的潜力。在每一个跨学科的研究工作中，对该领域的初步相互理解是以后工作成功的关键。在这项工作中，我们集中在水文降雨的要素-径流模型及其在洪水预报和预测径流量（河流中流动的水量）中的应用。本指南旨在帮助感兴趣的数据科学家了解问题，所涉及的水文概念以及沿途出现的细节。我们已经收集了我们在“加快速度”进行径流预测时学到的经验教训，并希望我们的经验对社区有用。
英文摘要：
In recent years, the paradigms of data-driven science have become essential components of physical sciences, particularly in geophysical disciplines such as climatology. The field of hydrology is one of these disciplines where machine learning and data-driven models have attracted significant attention. This offers significant potential for data scientists' contributions to hydrologic research. As in every interdisciplinary research effort, an initial mutual understanding of the domain is key to successful work later on. In this work, we focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow, the volume of water flowing in a river. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way. We have captured lessons that we have learned while "coming up to speed" on streamflow prediction and hope that our experiences will be useful to the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
