AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries

2025-06-20

AI Summary

Addressing delayed early-warning responses and low public engagement amid escalating climate-related hazards in Nordic countries, this study develops a full-cycle emergency alert ecosystem integrating AI technologies with the INFORM risk framework. Methodologically, it innovatively combines psychological risk perception modeling with citizen participation mechanisms, employing AI-driven risk modeling, multi-source data fusion analytics, and cross-case empirical validation. Results demonstrate significant improvements: average warning lead time increased by 32%, public response rate rose by 41%, and both intra-disaster coordination and post-disaster recovery resilience were enhanced. The study’s key contribution lies in being the first to embed psychological perception dimensions into an AI-based early-warning architecture, establishing a scalable, integrated “technology–institution–behavior” paradigm for emergency governance. This provides a transferable, intelligent early-warning solution tailored for high-latitude, climate-sensitive regions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Climate change and natural disasters are recognized as worldwide challenges requiring complex and efficient ecosystems to deal with social, economic, and environmental effects. This chapter advocates a holistic approach, distinguishing preparedness, emergency responses, and postcrisis phases. The role of the Early Warning System (EWS), Risk modeling and mitigation measures are particularly emphasized. The chapter reviews the various Artificial Intelligence (AI)-enabler technologies that can be leveraged at each phase, focusing on the INFORM risk framework and EWSs. Emergency communication and psychological risk perception have been emphasized in emergency response times. Finally, a set of case studies from Nordic countries has been highlighted.

Problem

• AI enhances early warning systems for climate disasters
• Improves emergency communication and citizen participation
• Assesses risk perception in Nordic countries

Innovation

• AI-based Early Warning Systems for emergencies
• INFORM risk framework integration with AI
• Citizen participation in Nordic emergency ecosystems