HydroNets：利用河流结构进行水文模拟
HydroNets: Leveraging River Structure for Hydrologic Modeling
摘要：
准确和可扩展的水文模型是从水资源管理到及时的洪水预警等几个重要应用的基本组成部分。然而，随着气候的变化，降水和径流模式的变化变得更加极端，准确的训练数据，可以解释由此产生的分布变化变得更加稀缺。在这项工作中，我们提出了一个新的家庭的水文模型，称为HydroNets，它利用河流网络结构。HydroNet是深度神经网络模型，旨在利用流域特定的径流信号和上游网络动态，这可以改善长期预测。河流结构先验知识的注入降低了样本的复杂性，即使只有几年的数据，也可以进行可扩展和更准确的水文建模。我们提出了一个实证研究，在印度的两个大流域，令人信服地支持所提出的模型及其优势。
英文摘要：
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the climate changes, precipitation and rainfall-runoff pattern variations become more extreme, and accurate training data that can account for the resulting distributional shifts become more scarce. In this work we present a novel family of hydrologic models, called HydroNets, which leverages river network structure. HydroNets are deep neural network models designed to exploit both basin specific rainfall-runoff signals, and upstream network dynamics, which can lead to improved predictions at longer horizons. The injection of the river structure prior knowledge reduces sample complexity and allows for scalable and more accurate hydrologic modeling even with only a few years of data. We present an empirical study over two large basins in India that convincingly support the proposed model and its advantages.
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。
