Traditional vs. AI-generated meteorological risks for emergency predictions.

Area of Science:

• Artificial Intelligence
• Meteorology
• Emergency Response

Background:

• Accurate prediction of firefighter interventions is crucial for effective emergency response.
• Traditional meteorological data sources may have limitations in critical situations requiring rigorous prioritization.
• Weather-related risks increasingly influence firefighter activities, necessitating precise meteorological information.

Purpose of the Study:

• To analyze and optimize firefighter prediction performance using Large Language Models (LLMs) for feature selection.
• To compare the efficacy of meteorological risk features extracted from Météo France versus those generated by LLMs.
• To investigate the impact of different feature extraction methods on predicting firefighter interventions.

Main Methods:

• Data preparation and comprehensive analysis of feature extraction approaches.
• Leveraging machine learning models (XGBoost, Random Forest, SVM) for prediction.
• Evaluating prediction results using two feature spaces: F1 (Météo France data) and F2 (LLM-generated data).

Main Results:

• Models trained with LLM-generated features (F2) consistently showed superior performance.
• Significant annual improvements in prediction accuracy were observed, especially for high and very high intervention activities.
• LLM-generated features were less effective for low intervention activities and underperformed in summer compared to traditional data.

Conclusions:

• LLM-based feature generation offers a promising methodology for enhancing firefighter prediction and resource management.
• This approach can accelerate response times and contribute to life preservation by reducing critical incident failure risks.
• Further research may refine LLM feature extraction for specific conditions like low-activity periods and seasonal variations.