283 citations · 310 across the 12 of their papers we have counts for
13 papers
Fine Flood Forecasts: Incorporating local data into global models through fine-tuning
Emil Ryd, Grey Nearing
Floods are the most common form of natural disaster and accurate flood forecasting is essential for early warning systems. Previous work has shown that machine learning (ML) models…
A Physically Driven Long Short Term Memory Model for Estimating Snow Water Equivalent over the Continental United States
Arun M. Saranathan, Mahmoud Saeedimoghaddam, Brandon Smith +3
Snow is an essential input for various land surface models. Seasonal snow estimates are available as snow water equivalent (SWE) from process-based reanalysis products or locally f…
AI Increases Global Access to Reliable Flood Forecasts
Grey Nearing, Deborah Cohen, Vusumuzi Dube +15
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely war…
A Machine Learning Data Fusion Model for Soil Moisture Retrieval
Vishal Batchu, Grey Nearing, Varun Gulshan
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (a…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
MC-LSTM: Mass-Conserving LSTM
Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz +5
The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related…
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
Daniel Klotz, Frederik Kratzert, Martin Gauch +4
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are…
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Sella Nevo, Gal Elidan, Avinatan Hassidim +4
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. Yet the majority of the worl…
Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network
Martin Gauch, Frederik Kratzert, Daniel Klotz +3
Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more gran…
Combining Parametric Land Surface Models with Machine Learning
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. The Hyb…
Using LSTMs for climate change assessment studies on droughts and floods
Frederik Kratzert, Daniel Klotz, Johannes Brandstetter +3
Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrolo…
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Frederik Kratzert, Daniel Klotz, Guy Shalev +3
Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade si…
Information vs. Uncertainty as the Foundation for a Science of Environmental Modeling
Grey Nearing, Hoshin Gupta
Information accounting provides a better foundation for hypothesis testing than does uncertainty quantification. A quantitative account of science is derived under this perspective…