论文标题
数据科学家的流量预测指南
A Data Scientist's Guide to Streamflow Prediction
论文作者
论文摘要
近年来，数据驱动的科学的范例已成为物理科学的重要组成部分，尤其是在地球物理学科（例如气候学）中。水文领域是这些学科之一，机器学习和数据驱动模型引起了极大的关注。这为数据科学家对水文研究的贡献提供了巨大的潜力。与每项跨学科研究工作一样，对域的最初相互了解是以后成功工作的关键。在这项工作中，我们着重于水文降雨的元素 - 运行模型及其在预测洪水和预测流量的应用，这是河流流动的水量。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一路上出现的细节。我们已经捕捉了我们在“加快速度”预测时学到的课程，并希望我们的经验对社区有用。
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.
