Author

Sanika Baste

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#reinforcement learning Open access Oct 2026

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Deterministic model predictions can struggle to adequately capture extreme events such as floods and droughts, which are of particular importance in hydrology. This limitation arises because deterministic models collapse the conditional discharge distribution to a single point estimate. Probabilistic modeling provides...

Sanika Baste, Sebastian Lerch, Daniel Klotz et al. · KITopen · 0 citations

#reinforcement learning Open access Oct 2026

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Abstract. Deterministic model predictions can struggle to adequately capture extreme events such as floods and droughts, which are of particular relevance in hydrology. This limitation arises because deterministic models collapse the conditional runoff distribution to a single point estimate. Probabilistic modeling pro...

Sanika Baste, Sebastian Lerch, Daniel Klotz et al. · Hydrology and earth system s... · 0 citations

#reinforcement learning Dataset Open access Sep 2026

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Code and results accompanying the EGUsphere preprint egusphere-2026-469. This repository implements deep-learning rainfall–runoff models over the CAMELS-CH basins, comparing a deterministic LSTM baseline against three probabilistic output heads — a DRN model (called LD-LSTM in the code), a Bernstein Quantile Network (B...

Sanika Baste · Zenodo (CERN European Organi... · 0 citations