Improving Flood Prediction and Warning through Probabilistic Deep Learning and Reinforcement Learning

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 provides a promising way to address this issue by explicitly representing uncertainty and assigning non-zero probabilities to a range of possible outcomes, including rare and extreme events, thereby capturing the full range of plausible hydrological responses. Motivated by this perspective, we investigate how long short-term memory (LSTM) based probabilistic models can be used for rainfall–runoff simulation across Switzerland. Overall, the probabilistic models show good calibration, although some miscalibration remains at the extremes. Differences between models mainly manifest in how uncertainty is distributed: some approaches produce narrower but lighter-tailed distributions, while others yield broader distributions with heavier tails. These trade-offs highlight that probabilistic models differ not only in sharpness but also in how they represent extreme outcomes. We also observe this trade-off in terms of the models’ single-point accuracy metrics. When evaluating the mean of the probabilistic predictions using the Nash–Sutcliffe efficiency (NSE), none of the probabilistic approaches outperform the deterministic LSTM in terms of average predictive accuracy. However, a clear advantage emerges when focusing on the tail of the discharge distribution. For the most extreme events (top 0.1% of the sorted discharge values), the deterministic LSTM underestimates more than 90% of observed values (since it provides estimates of an expectation), whereas probabilistic predictions can capture a substantially larger fraction of these extremes within their upper predictive bounds. Building on the additional information provided by probabilistic runoff predictions, we further show how such forecasts can be translated into discrete and actionable flood warnings using reinforcement learning. To this end, we introduce a Flood Risk Communication Agent (FRiCA) that operates on probabilistic runoff predictions and learns decision rules for issuing warnings of varying intensity. The FRiCA is implemented as an LSTM-based policy network and is trained by rewarding correct warning levels while penalizing the underestimation of flood severity. Results indicate that the FRiCA outperforms simple fixed heuristics, such as issuing warnings based on the predictive mean or a fixed high quantile (e.g., the 99th percentile). While this behavior already demonstrates the potential of reinforcement learning for improved flood risk communication, it also motivates future work toward more flexible and context-dependent decision strategies that adapt to varying hydrological and societal contexts.