HydroNet: leverage River Structure for Hydrologic Modeling and Flood Prediction
Details
The talk is based on the paper: “HydroNets: Leveraging River Structure for Hydrologic Modeling” recently published at ICLR 2020, Workshop on AI for Earth Sciences. In this talk, Zach, the paper's author, will present HydroNets, a family of deep neural network models designed for hydro-logic forecasting.
Paper: https://arxiv.org/abs/2007.00595
Lecture abstract:
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 the 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.
Presenter BIO:
Zach Moshe is an ML researcher at Google Research, primarily focused on the flood forecasting initiative where he develops hydrological models that are used to predict future flooded areas and warn users in these areas before. Zach is passionate about ML in general and applications of it in unique domains.
More work and information can be found at: http://zachmoshe.com/
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