Another approach for handling multiple frequencies in a single LSTM based model.
Posts
We use the framework of conformal prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions.
Comparing LSTMs to hybrid deep learning models on generalization capabilities during extreme events.
In this paper, we present the results of the “Rate My Hydrograph” study, where we compare expert ratings of simulated hydrographs with quantitative metrics.
The persistence of errors: How evaluating models over data partitions relates to a global evaluation
Oral presentation for EGU General Assembly 2023. This is one about a certain phenomena that appears when we evaluate a model over subsets of the data. Eventually the plan is to make a technical note out of it.
In this paper, we present the full operational framework used by the Flood Forecasting team at Google.
This paper performs a rigorous benchmark of traditional hydrologic models and an LSTM-based model for rainfall-runoff modeling.
This paper introduces the Caravan dataset, a global large-sample hydrology dataset that builds on cloud computing to be extensible by anyone.
Oral presentation at the EGU General Assembly 2022 on a social study to compare expert rankings of simulated hydrographs with quantitative metrics.
Accompanying paper to our open source Python library NeuralHydrology.
This paper investigates the hypothesis that the lack of enforced mass conservation is the main reason that deep learning models outperform traditional hydrology models.
Presentation at the AGU 2021 Fall Meeting presenting approaches to improve LSTM streamflow predictions with near-real-time observation data.
In this paper, we investigate the potential of using the LSTM as a post-processor for the US National Water Model.
Technical note that compares autoregression to data assimilation for deep learning models and rainfall-runoff modeling.
This paper investigates the hypothesis that deep learning models may not be reliable in extrapolating extreme events.
In this paper we show the benefits of using multiple meteorological forcing products at the same time in a single LSTM-based rainfall-runoff model over just using a single product.
Oral presentation at the virtual EGU General Assembly 2021 on rainfall–runoff forecasting with Multi-Timescale LSTM.
Oral presentation at the virtual EGU General Assembly 2021 on rainfall–runoff prediction with Graph Neural Networks.
Oral presentation at the virtual EGU General Assembly 2021 on uncertainty prediction with LSTMs in the context of rainfall-runoff modeling.
Deep learning based uncertainty estimation techniques and benchmarking procedure for rainfall-runoff modeling.
LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins in the upper Danube catchment and Austria.
In this study, we present a mass-conserving variant of the LSTM and its application to arithmetic tasks, traffic forecasting, modeling a pendulum and rainfall-runoff modeling.
Oral presentation at the virtual AGU Fall Meeting 2020 on streamflow prediction at arbitraty timescales with a single LSTM-based model.
Oral presentation at the virtual AGU Fall Meeting 2020 on uncertainty prediction with LSTMs in the context of rainfall-runoff modeling.
Spotlight talk at the AI for Earth Sciences workshop of the NeurIPS 2020, presenting introduction of the world and terminology of hydrology/streamflow prediction for data scientists.
Spotlight talk at the AI for Earth Sciences workshop of the NeurIPS 2020, presenting a first glimpse of a new LSTM-based model that conserves mass by design: the Mass-Conserving LSTM
Opinion paper discussing the future of Hydrology, especially in the context of recent developments in Machine Learning
Invited talk at the EGU General Assembly 2020 on using LSTMs for flood forecasting in ungauged basins.
Virtual presentation at the EGU General Assembly 2020 comparing LSTMs trained for each basin individually with a single LSTM trained for all basins together.