Searched for: subject%3A%22lstm%22
(1 - 1 of 1)
document
Wilbrand, K. (author), Taormina, R. (author), ten Veldhuis, Marie-claire (author), Visser, Martijn (author), Hrachowitz, M. (author), Nuttall, Jonathan (author), Dahm, Ruben (author)
Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually...
journal article 2023