An evidential linguistic CoCoSo method for early warning system technology selection: A risk-aware emergency decision-making approach

This study presents an improved evidential linguistic CoCoSo method designed to strengthen disaster risk reduction by enhancing the selection of early warning system (EWS) technologies. It addresses persistent challenges in emergency decision‑making, including uncertainty in expert judgement, incomplete information, and the influence of subjective risk attitudes. The authors refine the evidential linguistic term set to reduce computational complexity and better reflect real‑world expert hesitation, while introducing a risk‑attitude correction coefficient and a new evidence‑distance measure to manage conflicting evaluations. The method is demonstrated through a case study on multi‑hazard EWS technology selection in Sichuan Province, China, offering insights relevant to emergency managers, policymakers, and researchers working on risk‑aware EWS design and implementation. 

The study recommends adopting structured, risk‑aware multi‑attribute decision‑making tools to improve the reliability of EWS technology choices. It highlights the value of integrating expert risk attitudes into evaluation models, using enhanced linguistic representations to capture uncertainty more accurately, and applying reliability‑based information fusion to reduce bias. The authors suggest that emergency management authorities can strengthen preparedness by systematically applying such methods when comparing technical alternatives, particularly in multi‑hazard contexts. The lessons learned from the Sichuan case study indicate that combining evidential linguistic modelling with CoCoSo aggregation can support more defensible, transparent, and effective decisions in disaster risk reduction planning.