Beyond the richter scale: a fuzzy inference system approach for measuring objective earthquake risk.

Highlights: Introducing a novel Fuzzy Inference System (FIS) approach that integrates satellite and GIS data for precise earthquake risk mapping and minimizing uncertainty. Achieving comprehensive earthquake risk assessment through the integration of diverse data sources. Enhancing the credibility of modeling results by validating with historical earthquake data, providing valuable insights for policymakers addressing natural hazards. Earthquakes pose significant natural hazards and impact populations worldwide. Iran is among the most susceptible countries to seismic activity, making comprehensive earthquake risk assessment crucial. This study employs geospatial methods, including integrating satellite, ground-based, and auxiliary data to model earthquake risk across this country. A Fuzzy Inference System (FIS) is used to generate earthquake hazard probability and vulnerability layers, considering factors such as slope, elevation, fault density, building density, proximity to main roads, proximity to buildings, population density, and earthquake epicenter, magnitude, proximity to the epicenter, depth density, peak ground acceleration (PGA). The results highlight high-risk areas in the Alborz and Zagros Mountain ranges and coastal regions. Moreover, the findings indicate that 39.7% (approximately 31.7 million people) of Iran's population resides in high-risk zones, with 9.6% (approximately 7.7 million) located in coastal areas vulnerable to earthquakes. These findings offer valuable insights for crisis management and urban planning initiatives. [ABSTRACT FROM AUTHOR]