Extreme weather and climate events have become a pressing global concern in recent decades, posing significant hazards and leading to major disasters when they intersect with vulnerable populations and natural systems. According to some estimates , natural disasters pose an average annual economic loss of between USD 250 billion and USD 300 billion, with nearly two-thirds of such disasters being caused by extreme weather hazards. Among these events, flooding emerges as one of the main causes of disasters, along with storm surges and windstorms. Flood frequency and intensity are increasing, driven primarily by climate change , and the growing socio-economic and environmental impacts of floods demand effective risk management strategies to mitigate their consequences.

Traditionally, Disaster Risk Management (DRM) has relied on hazard-centric approaches for the prediction and monitoring of natural disasters by modelling physical weather phenomena . While effective for understanding meteorological risks, these methods often overlook the socio-economic factors that ultimately determine the real impacts. However, recent trends advocate for a shift toward impact-based forecasting , which aims to anticipate consequences by incorporating data on vulnerability and exposure. This paradigm shift can allow for more targeted decision-making, encouraging proactive responses from all levels of society .

The transition toward impact-based forecasting can be understood as a data-integration challenge between very diverse sources. Artificial intelligence (AI) and machine learning (ML) are technologies that excel in processing vast and complex datasets, identifying patterns, and generating useful insights. With this in mind, ML has already proven effective in resource allocation and anticipatory actions , and in weather forecasting .

In this work, we propose an ML methodology for predicting flood-related impacts, using emergency calls as the target variable. We focus on Catalonia, in northeastern Spain. The region has a typical Mediterranean climate, resulting in periods of drought and recurring flood events, including intense flash floods. Our methodology works at the municipal level and with hourly temporal resolution between October 2018 and February 2025.

Emergency calls can serve as a very valuable impact indicator, as they represent one of the most immediate signals of population distress, particularly during early stages of flooding. However, it is important to note that the population-dependent nature of these data (they depend on people reporting) makes them heterogeneous both across time (occurring usually during hazardous events) and territory, following the population distribution. From an ML perspective, this results in a highly imbalanced problem, particularly in less populated areas. We address this problem by stratifying models based on population densities, allowing for a better assessment of performance based on the data availability, as this stratification also groups localities with similar urban or rural dynamics.

The main objective of this study is to demonstrate how ML pipelines integrating data on impacts, vulnerability and exposure can produce impact predictions that are more actionable than current operational weather-based systems. We evaluate the methodology's capacity to increase detection power and reduce false alarms when compared to hazard-based baselines (e.g., official weather warnings) across different population densities. Furthermore, we analyse how performance depends on data availability, highlighting the main limitations of impact modelling in less populated regions.

Impact and risk assessment for floods has traditionally been approached by combining hazard-based models with static layers of vulnerability and exposure. An example in flash floods is the ReAFFIRM method (Real-time Assessment of Flash Flood Impacts; ), which quantifies impacts on population, economic losses, and affected critical infrastructure. This methodology was later expanded to the pan-European level in ReAFFINE (Real-time Assessment of Flash Flood Impacts at paN-European scale; ). For pluvial floods, introduced a site-specific framework (SS-EWS) to guide the design of community-based, impact-focused early warning systems. Furthermore, developed a methodology for forecasting the impacts of compound floods by leveraging predictions from both fluvial and flash flood models. The European Flood Awareness System (EFAS; , ) currently integrates operational products developed under the TAMIR project (Advanced Tools for pro-Active Management of Impacts and Risks Induced by Convective Weather, Heavy Rain and Flash floods in Europe; ); and it is planned to incorporate tools from EDERA (Early warning Demonstration of pan-European rainfall-induced impact forecasts; ; ), the successor to TAMIR. Together, these systems can enhance the anticipation and management of the risks posed by heavy-rainfall-induced events across Europe.

Although less common than physical methods, machine learning (ML) methodologies are increasingly being explored for impact assessment. In flood risk management, AI approaches enable automated mapping and dynamic inundation forecasting, which are critical for real-time warnings and resilience planning in vulnerable urban areas . For instance, explored the probabilistic prediction of flash-flood-related human incidents and fatalities using random forest classifiers. Moving to the topic of tropical cyclones, an early example of ML-based impact forecasting was developed by the 510 initiative of the Netherlands Red Cross to predict the humanitarian impact of typhoons , classifying municipalities based on whether more than 10 % of houses were completely destroyed or not. This approach was later improved by , adapting the methodology to work with globally available data. Additionally, developed a multi-hazard impact modelling framework for economic loss assessment. Moreover, some works have also seen success in modeling weather-related impacts on airport disruptions . The application of ML tools in the context of disaster risk reduction is promising. However, as highlighted by , these systems should not replace human decision-making, but rather serve as decision support tools that must be implemented ethically and responsibly. Consequently, explainable AI (XAI) techniques are increasingly used to improve the trust and transparency of these models, mitigating the “black-box” problem and helping to understand the links between hazards and susceptibility . Techniques like SHAP and LIME are commonly paired with machine learning approaches to identify key predictors and provide interpretability. For example, in hurricane risk assessment, employed LIME alongside deep learning methodologies to offer actionable knowledge to stakeholders, improving proactive risk mitigation.

To train such impact models requires robust and comprehensive datasets. Emergency calls, as high-resolution indicators of population distress, serve as an excellent proxy for real-time impacts and have been used in the past to approximate weather damage. Early studies, such as , used calls to correlate hailstone size with on-the-ground damage in Australia. use emergency calls in the context of convective storms, introducing a methodology capable of tracking the storm path and determining the hazard level. More recently, several case studies have demonstrated the value of calls for refining flood risk mapping and creating impact catalogues . The utility of these types of data have also been proven for relating the impact of rainfall on road accidents through ambulance dispatches , and for developing a framework for exploring urban vulnerability dynamics through ambulance calls . However, access to emergency call data, and impact data in general, is severely limited. To address this gap, presented a comprehensive methodology for building such catalogues, including emergency calls. The framework was later operationalised by the Basque Meteorological Agency. But even when available, impact data tend to be sparsely distributed both temporally and spatially due to the infrequent nature of impactful weather events, but also underlying socio-economical characteristics . The study conducted by shows how impacts are predominantly concentrated in major urban areas, suggesting that population density and regional characteristics influence recorded incidences. To account for this, we explore a methodology for training distinct models based on population density.

Closest to our work is the study by , which also employs ML and emergency call data in Catalonia to make predictions at the municipality level, with great success compared to traditional daily meteorological warnings. However, their model targets multiple weather phenomena simultaneously and operates at a daily resolution, potentially missing fast-occurring events such as flash floods, which require finer temporal resolutions and are particularly common in the region, often resulting in significant losses . Our work aims to advance this line of research by producing hourly predictions focused specifically on flood-related impacts, offering more granular and timely information for emergency response.

This section describes the study area, focusing on its geographical and climatological characteristics and the distribution of its population. The chosen spatial and temporal resolutions for the study are also presented.

To successfully model impact, it is essential to provide relevant and diverse information so the model can identify relationships between input data and the ultimate impacts. This section describes how our dataset is created, including the integration of static and dynamic data sources at the study's resolution and the partitioning of the data based on population density groups. In the end, 54 input features are created (see Table for the full listing).

In this section, we describe the methodology followed in the study. This includes the definition of the baselines, relevant metrics, and the evaluation setup.

Table shows the mean and standard deviation of test set results. Performance improves drastically with population density. In particular, regarding the Critical Success Index (CSI), while the low-density model struggles to surpass 1 % (0.83 %), medium- (9.52 %) and high-density (28.77 %) models achieve more competitive results. This is also true for the precision and recall metrics. It is worth mentioning that these results are obtained using the group-specific impact thresholds from Sect. , so population density is not the only factor influencing performance. However, because the low- and medium-density groups both share the same impact threshold of 1 call, the superior results of the medium-density model confirm the underlying effect of population density.

A similar trend is observed in the standard deviation. The low-density model shows very high variance (± 0.92 % CSI), exceeding its own mean value. As the population increases, models become more stable, with standard deviation values that are relatively smaller compared to the mean. However, the absolute SD intervals remain significant even for the best-performing models.

In Fig. , we show the performance of the high-density model across the different temporal configurations (test set results), along with a bootstrap variance estimate for each test set. Additionally, we display the overall mean (dotted line) and standard deviation (grey strip) across all configurations. Although no clear trend is observed, we note how the standard deviation intervals of individual configurations (indicated by the error bars) closely align with the overall performance variability across all three metrics. This suggests that once intra-event correlation is accounted for through block bootstrapping, uncertainty within specific time windows matches the overall variability across all temporal configurations. It should be noted, however, that these two types of intervals reflect different sources of uncertainty, with bootstrapping capturing intra-event dynamics, while the overall standard deviation captures performance fluctuations across temporal configurations.

In this work, we have explored how machine learning (ML) models can be used for the hourly prediction of flood-related impacts at the local scale, using high-resolution emergency call data as an impact proxy. To address the varying data availability across the territory, municipalities are divided into three population density groups (Low, Medium, and High), and individual models are trained for each.

We find that ML has great potential for localised impact anticipation where sufficient data exists. Even with a severe class imbalance (a positive rate of around 0.02 %), our high- and medium-density models successfully learn to extract meaningful patterns from the data, improving current methods. However, in extremely low-data scenarios, such as sparsely populated areas like our low-density group, traditional hazard-based methods remain a more reliable tool for identifying general regions of risk. Given the low volume of emergency calls in these areas, training data-driven models with current datasets does not seem realistic.

We compare the proposed ML approaches against traditional, hazard-based methodologies currently in use: official weather warnings issued by the national (AEMET) and regional (SMC) meteorological agencies, and a radar-based river warning product (FF-EWS) used by Catalan civil protection authorities. For high-density areas (containing over 92 % of the total population), the model surpasses all baselines by a large margin across all performance metrics. In medium-density areas, the ML model also substantially outperforms the baselines, notably in achieving a precision of 15.47 %, compared to the 2.30 % of the best-performing baseline. For low-density municipalities, the ML approach obtains the best average Critical Success Index, but this is driven mainly by a higher precision, while its recall is the lowest among all approaches (4.29 %).

explored a similar problem in Catalonia also using 112 emergency calls, proving strong predictive capabilities for severe weather impacts at a daily temporal resolution and municipal scale. The transition to an hourly temporal resolution, despite only targeting flood-related emergencies, allows us to provide more timely and actionable insights into incoming impacts, thereby improving anticipation and readiness. Moreover, our strategy of providing distinct models based on population density allows different behaviours to emerge based on local characteristics. However, shifting to a higher resolution exacerbates the data imbalance problem, as the total number of samples increases significantly while the number of observed impacts remains similar, leading to a higher sparsity. To address this, targeting multiple weather phenomena simultaneously could be beneficial, as it would allow the model to leverage a larger set of impact indicators.

Additionally, we examined the behaviour of the different approaches during different phases of a rainfall event. We observe that all approaches struggle during the first hour of rainfall, showing reduced performance compared to that of the following hours. This is expected, as predicting the onset of impacts is arguably more challenging than predicting their continuation. However, the high-density model still obtains better results than traditional hazard-based methods in this situation, and once the event has started, the performance rises and is maintained even after the rainfall stops, showing promising results for tracking unfolding impacts. Pairing this with explainable AI techniques like SHAP allows for the interpretability of model outputs. In low- and medium-density areas, rainfall-related features appear to have a greater average contribution to the predictions than other variables, largely ignoring static indicators about population. In contrast, the high-density model achieves the most accurate predictions by modulating information on emergency calls in previous hours with weather data and population counts. These findings highlight how interpretability can vary significantly by demographic region. However, since the reliability of SHAP values depends on the model's prediction capabilities, the interpretations from medium- and especially the low-density models must be understood as exploratory insights rather than robust conclusions. This variability underscores the necessity of studying the behaviour of ML systems under different conditions, to correctly support decision-making, instead of simply outputting “solutions”. Ultimately, this analysis intends to build trust in these algorithms by addressing the “black-box” problem , a requisite for the operational application of any ML-based approach in real-time Disaster Risk Management contexts.

With this paper, we have not only demonstrated the effectiveness of ML for improving impact anticipation, but also highlighted a critical challenge that affects both the adoption of ML-based models, and the broader transition from hazard-based forecasting towards localised impact-based systems: the need for systematic frameworks to collect, share, and integrate diverse impact data sources, such as emergency calls, insurance claims or infrastructure impacts . We argue that combining multiple data sources can overcome the individual limitations of each (for example, the dependence of emergency calls on people reporting) and ultimately build a more complete and homogeneous picture of actual societal impacts. In practice, this translates into two paths for implementation: in data-rich urban areas, models like ours could already be operationalised to improve impact anticipation and warnings, while in more rural regions with data scarcity, the focus should shift towards building the foundational data catalogues this work proves are essential.