AI outperforms traditional weather systems in emergency forecasting

While LLM-based features improved performance for high-risk interventions, they proved less effective during low-activity periods and underperformed in the summer season, indicating seasonal calibration remains necessary. Although LLMs provided better risk granularity, they struggled with consistency in low-intensity or summer-period events, the authors noted.

Artificial intelligence-generated weather risk assessments have surpassed traditional meteorological data in forecasting firefighter interventions, according to a new study published this week in Frontiers in Artificial Intelligence. The research, titled "Traditional vs. AI-generated meteorological risks for emergency predictions", demonstrates that large language models (LLMs) can enhance emergency response predictions, offering faster and more accurate resource deployment under high-risk conditions.

Led by Naoufal Sirri and Christophe Guyeux of the University of Franche-Comtéthe study evaluated nine years of data from 334,536 emergency interventions in Doubs, France, between 2015 and 2024. Researchers compared the predictive performance of AI-generated meteorological risk features, derived using OpenAI’s LLMs, with traditional risk data sourced from Météo-France. The aim was to identify which approach better supports emergency forecasting in high-pressure operational environments.

Using three machine learning models, XGBoost, Random Forest, and Support Vector Machines, the team tested two sets of features: one combining general data with Météo-France weather alerts (F1), and the other combining general data with LLM-generated meteorological risk interpretations (F2). Models trained on F2 consistently outperformed those using F1, particularly when predicting high and very high firefighter activity levels.

XGBoost trained on F2 achieved the highest overall performance with a precision of 0.74, recall of 0.75, an F1-score of 0.79, and an area under the curve (AUC) of 0.91. In contrast, the same model trained on F1 had a lower F1-score of 0.72 and AUC of 0.85. Similar patterns were observed across the other models, with LLM-based features boosting predictive accuracy across multiple performance metrics.

The study's results suggest that AI-generated features, extracted from weather bulletin text via natural language prompts, capture nuanced information often missed by standardized systems. By leveraging LLMs to analyze textual weather alerts, researchers were able to classify meteorological risks on a color-coded scale (green to red) and integrate this structured data into predictive models.

While LLM-based features improved performance for high-risk interventions, they proved less effective during low-activity periods and underperformed in the summer season, indicating seasonal calibration remains necessary. Although LLMs provided better risk granularity, they struggled with consistency in low-intensity or summer-period events, the authors noted.

Traditional meteorological data from Météo-France offered more conservative risk assessments but demonstrated greater stability across low-risk periods. In contrast, LLM outputs reflected wider variability due to their sensitivity to language in weather bulletins and lack of calibrated thresholds.

Despite this, the authors found that LLM-generated features offered substantial operational benefits. Their inclusion significantly improved model accuracy during critical scenarios, potentially reducing response times and optimizing the deployment of firefighting personnel and resources.

The study also emphasizes cost and accessibility advantages. While LLMs are computationally intensive, they eliminate the need for costly high-resolution meteorological datasets, offering a scalable solution for emergency forecasting in regions with limited infrastructure.

To construct the feature spaces, researchers integrated general variables such as school calendars, holidays, solar activity, epidemiological data, air quality, and satellite imagery. Meteorological vigilance data from both Météo-France and LLM analysis were layered on top, forming the foundation for comparative model training.

Following pre-processing and feature selection, including Pearson correlation, chi-square ranking, and gradient boosting techniques, the models underwent optimization via Bayesian tuning and were validated using five-fold cross-validation. This rigorous approach ensured that the final results were robust and generalizable across different data subsets.

The results confirmed that F2-based models are particularly suited for predicting spikes in emergency activity. Monthly error analysis and annual F1-score tracking revealed steady improvements over time when using AI-generated weather risk features. However, the study noted that no significant gains were observed for moderate or low-risk interventions.

Beyond statistical performance, the study highlights the strategic implications of using AI for emergency preparedness. Improved prediction of extreme events could allow fire services to anticipate peak demand, pre-position equipment, and coordinate multi-site responses more effectively.

The authors acknowledge limitations, including the dependence of LLM-generated features on text input quality and prompt design. The lack of standardized calibration and the variability of natural language remain potential sources of inconsistency. Future research will explore integrating multimedia inputs - such as satellite data and media reports - into LLM pipelines and using neural network architectures to further refine prediction accuracy.

Ultimately, the study calls for targeted application of AI-enhanced forecasting tools. While LLM-derived weather risks offer significant gains in high-impact scenarios, traditional meteorological data remains more reliable for low-intensity and routine forecasting. By tailoring risk analysis tools to operational context, emergency services can combine the strengths of both approaches to improve readiness and resource management.