A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling

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Authors

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, Grey Nearing 

Abstract

A deep learning rainfall-runoff model can take multiple meteorological forcing products as inputs and learn to combine them in spatially and temporally dynamic ways. This is demonstrated using Long Short Term Memory networks (LSTMs) trained over basins in the continental US using the CAMELS data set. Using multiple precipitation products (NLDAS, Maurer, DayMet) in a single LSTM significantly improved simulation accuracy relative to using only individual precipitation products. A sensitivity analysis showed that the LSTM learned to utilize different precipitation products in different ways in different basins and for simulating different parts of the hydrograph in individual basins.

DOI

https://doi.org/10.31223/osf.io/pjm5a

Subjects

Earth Sciences, Hydrology, Physical Sciences and Mathematics

Keywords

machine learning, Deep learning, hydrology, modeling, Rainfall-Runoff

Dates

Published: 2020-05-08 17:31

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License

CC BY Attribution 4.0 International

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