Search Results for author: Grey Nearing

Found 8 papers, 3 papers with code

MC-LSTM: Mass-Conserving LSTM

1 code implementation13 Jan 2021 Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer

MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time.

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.

ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach

no code implementations29 Nov 2020 Sella Nevo, Gal Elidan, Avinatan Hassidim, Guy Shalev, Oren Gilon, Grey Nearing, Yossi Matias

Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm.

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.

Combining Parametric Land Surface Models with Machine Learning

no code implementations14 Feb 2020 Craig Pelissier, Jonathan Frame, Grey Nearing

A hybrid machine learning and process-based-modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state.

Gaussian Processes

Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets

1 code implementation19 Jul 2019 Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, Grey Nearing

The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone.

Time Series

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