Optimizing seismographic network strategies for earthquake surveillance under linguistic pythagorean fuzzy dynamic knowledge

Global seismographic networks (GSNs) capture fine details of ground motion signals, assisting scientists and governments in detecting and characterizing earthquakes and other seismic events worldwide. Continued improvements in these networks enhance knowledge about Earth structure, atmospheric interaction, and geological hazards. Because seismic stations operate under changing environmental and operational conditions, decision-making for network optimization requires tools that can handle uncertainty, linguistic assessments, and time-varying information. This study develops dynamic Dombi aggregation operators within the linguistic Pythagorean fuzzy set (LPFS) framework to support multi-attribute decision-making in such evolving environments. Aggregation operators combine multiple pieces of information into a single representative assessment. Their dynamic versions allow the influence of past and present information to vary over time, reflecting changes in data reliability and expert preference. The Dombi formulation further introduces a flexible parameter that models the decision-maker’s risk attitude. Two new operators are proposed. The linguistic pythagorean fuzzy dynamic Dombi weighted averaging (LPFDyDWA) operator produces a time-adjusted average evaluation in which both attribute importance and temporal relevance are incorporated under linguistic uncertainty. The linguistic pythagorean fuzzy dynamic Dombi weighted geometric (LPFDyDWG) operator performs a similar integration using a geometric combination, which is particularly suitable when interactions among attributes are multiplicative or mutually reinforcing. Together, these operators provide adaptive and interpretable tools for aggregating uncertain, language-based evaluations across multiple time periods. We establish their structural properties, introduce a novel score function for ranking alternatives, and develop a systematic decision-making procedure. The effectiveness of the proposed framework is demonstrated through an application to the optimization of seismographic network performance for enhanced earthquake monitoring, along with comparative analysis against existing methods.