Presenters-0670

From CSDMS
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI


Long Short-Term Memory networks for rainfall-runoff modeling



Frederik Kratzert

Google Research, Austria
kratzert@google.com


Abstract
Long Short-Term Memory networks (LSTMs) have been around since the early 90’s but only in the last few years, LSTMs gained increasing popularity in hydrological sciences. Publication counts see exponential growth and LSTMs power some of the largest-scale operational flood forecasting systems. In this presentation, we look at some of the research from the past few years and try to understand why the LSTM is a particularly well suited architecture for the application as rainfall-runoff model but also discuss limitations and open research questions.

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Of interest for:
  • Education and Knowledge Transfer (EKT) Working Group
  • Hydrology Focus Research Group
  • Artificial Intelligence & Machine Learning Initiative
  • River Network Modeling Initiative