Recurrent Neural Networks and Geostatistics Applied to the Prediction of Severe Rainfall Events and Anomaly Detection

The intensification of the frequency and severity of precipitation events has had a significant impact on densely populated urban areas, highlighting the need to improve traditional weather forecasting models. Due to the dynamic nature and interaction of atmospheric, oceanic, and terrestrial factors associated with these phenomena, forecasting these events is complex and challenging. Methods based on recurrent neural networks have surpassed traditional techniques in forecasting intense precipitation. However, challenges remain, such as measurement uncertainty and the high variability of events characterized by non-stationary phenomena. In this study, we propose a predictive model that employs recurrent neural networks trained exclusively with severe rainfall events.

The methodology developed incorporates Kriging for modeling the spatial structure of precipitation, allowing values to be estimated in locations without measurements and generating continuous rainfall fields that feed the forecast model. To capture the temporal evolution and abrupt variability associated with severe events, we use recurrent neural networks structured with sliding time windows of different sizes.  This combination seeks to exploit the spatial correlation of the data and the learning capacity of time series to refine anomaly detection. The proposed approach was applied to the Metropolitan Region of Rio de Janeiro, a scenario marked by strong geomorphological complexity and high recurrence of extreme events. The results show that the integration between geostatistical interpolation and neural networks substantially improves the system's ability to capture rapid spatiotemporal variations in precipitation, which can assist risk warning systems and mitigate the socioeconomic impacts associated with these events.