Virtual presentation at the EGU General Assembly 2020 presenting the HydroNets architecture for modeling entire river networks.
Posts
Modeling entire nested river trees by integrating the river hierachy into the neural network architecture. This manuscripts proposes HydroNets, an architecture designed for modeling multiple nested gauge stations.
Workshop paper, investigating first ways of using LSTM-based models for climate change related questions in hydrology.
Workshop paper, investigating first ways of using LSTM-based models for climate change related questions in hydrology.
Poster at the AGU 2019 Fall Meeting presenting Martins work on a model comparison study for the Great Lakes/Lake Erie area.
Presentation at the AGU 2019 Fall Meeting presenting our recent results on large-scale hydrological modeling using LSTMs.
In this manuscript we test LSTM-based rainfall-runoff models on the task of prediction in ungauged basins and show, that a single LSTM-based model does better prediction in ungauged basins than a traditional hydrological model that was specifically calibrated for each basin individually.
This paper investigates the influence of the number of training basins and the training period length on the model performance for the EA-LSTM and XGBoost
In this manuscript we show for the first time how to train a single LSTM-based neural network as general hydrology model for hundreds of basins. Furthermore, we proposed the Entity-Aware LSTM (EA-LSTM) in which static features are used explicitly to subset the model for a specific entity (here a catchment).
PICO presentation at the EGU General Assembly 2019 on prediction in ungauged basins using LSTM based models.
Poster presentation at the EGU General Assembly 2019 on uncertainty estimation using MC-Dropout and LSTMs.
Video presentation in CUAHSI’s 2019 Spring Cyberseminar Series on Recent advances in big data machine learning in Hydrology.
Book chapter in the Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Editors Wojciech SamekGrégoire MontavonAndrea VedaldiLars Kai HansenKlaus-Robert Müller).
Presentation at the AGU 2018 Fall Meeting on experiments regarding the interpretability of LSTM states.
NeurIPS 2018 workshop paper, showing first results on using LSTMs for prediction in ungauged basins.