Search Results for author: Martin Gauch

Found 7 papers, 5 papers with code

AI Increases Global Access to Reliable Flood Forecasts

1 code implementation30 Jul 2023 Grey Nearing, Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo, Florian Pappenberger, Christel Prudhomme, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Yoss Matias

Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System).

Management

Conformal Prediction for Time Series with Modern Hopfield Networks

1 code implementation NeurIPS 2023 Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter

To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains.

Conformal Prediction Time Series

Few-Shot Learning by Dimensionality Reduction in Gradient Space

1 code implementation7 Jun 2022 Martin Gauch, Maximilian Beck, Thomas Adler, Dmytro Kotsur, Stefan Fiel, Hamid Eghbal-zadeh, Johannes Brandstetter, Johannes Kofler, Markus Holzleitner, Werner Zellinger, Daniel Klotz, Sepp Hochreiter, Sebastian Lehner

We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace.

Dimensionality Reduction Few-Shot Learning

Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling

no code implementations15 Dec 2020 Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Günter Klambauer, Sepp Hochreiter, Grey Nearing

Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.

Benchmarking

Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network

1 code implementation15 Oct 2020 Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, Sepp Hochreiter

Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy.

A Data Scientist's Guide to Streamflow Prediction

no code implementations5 Jun 2020 Martin Gauch, Jimmy Lin

In recent years, the paradigms of data-driven science have become essential components of physical sciences, particularly in geophysical disciplines such as climatology.

The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction

1 code implementation17 Nov 2019 Martin Gauch, Juliane Mai, Jimmy Lin

Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements.

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