Automated spatio-temporal detection of medicane hazards and socio-economic impacts from news-based data using machine learning

Mediterranean tropical-like cyclones, known as medicanes, are among the most damaging and socio-economically disruptive weather phenomena in the region. While their physical characteristics have been increasingly investigated, a comprehensive and systematic assessment of their societal and economic impacts remains limited, largely due to the fragmented and heterogeneous nature of impact information. 

To address this gap, we present an automated, AI-based framework to detect, classify, and monitor the socio-economic impacts associated with medicanes using unstructured textual data from diverse sources, including news articles, media reports, and documentation from international agencies. The methodology follows a two-stage workflow. First, event-related texts are identified through an advanced filtering procedure combining geographical constraints, temporal consistency, topic relevance, and keyword-based selection. Second, state-of-the-art Natural Language Processing (NLP) and Machine Learning (ML) techniques are applied to extract, classify, and quantify reported hazards and impacts across multiple sectors, such as infrastructure, population, economic activities, and emergency response. 

By integrating NLP and ML methods with geolocation tools, the framework enables the automated spatio-temporal mapping of medicane related hazards and damages, substantially reducing subjectivity and dependence on manual post-event assessments. The approach demonstrates that news-based and other textual sources can serve as consistent, scalable, and near-real-time indicators of the socio-economic consequences of complex multi-hazard events such as medicanes.

This work provides, to our knowledge, the first systematic and reproducible methodology to quantify the socio-economic footprint of Mediterranean cyclones using text-as-data approaches. The results highlight the potential of NLP-based impact detection to complement traditional hazard-focused analyses and to support integrated risk assessment, climate services, and disaster risk reduction strategies in the Mediterranean region.