Using machine learning for the prediction of flood-related 112 calls

Abstract. As weather-related disasters become more frequent and severe, there is a growing global push toward impact-based early warning systems, exemplified by initiatives such as EW4All. This transition positions machine learning (ML) and artificial intelligence (AI) as powerful tools for integrating meteorological hazard data with information on vulnerability and exposure into data-driven forecasting systems. In this work, we explore the use of 112 emergency calls as high-resolution impact proxies for an ML-based prediction problem. Specifically, we develop a model that combines rainfall-related weather data, information on recent emergencies, and static vulnerability-exposure layers to predict, at a municipal and hourly resolution, whether flood-related impacts will occur in the next hour. This study covers the period between October 2018 and February 2025 in Catalonia, northeastern Spain. To address the severe temporal class imbalance and uncertainty characteristics of emergency call data, we define a custom walk-forward evaluation scheme that ensures the same number of positive samples across comparable time periods. We then distribute municipalities into three distinct population density groups (low, medium, and high) and train one model for each one. This stratification enables us to evaluate performance across diverse population dynamics and varying data availability. The resulting models are compared against operational methodologies, such as climatology-based weather warnings issued by meteorological agencies. Our results show that the ML approach represents a substantial improvement in two out of the three groups. The model for the lowest-density group, however, struggles due to a substantial lack of impact data, highlighting a key roadblock for data-driven algorithm development in sparsely populated regions. Further experiments focus on explainability to improve trust by understanding the models' behaviours using feature importance analyses like SHAP (SHapley Additive exPlanations), feature group selection, and ablation. Additionally, we present a methodology to evaluate model behaviour across different stages of a rainfall event. These experiments show how ML models can effectively combine various data sources to provide more effective predictions than using these sources in isolation. Moreover, while the best-performing model exhibits moderate performance at the onset of rain, it still consistently improves upon traditional baselines. As the event evolves, its prediction skill rises significantly and is maintained even after precipitation has stopped. This highlights the strong potential of even relatively simple ML pipelines to deliver timely, localised anticipation of weather-related impacts.