An Innovative Fusion of New Smile Random Forest and Fuzzy Analysis for Comprehensive Seismic hazard Assessment: A Case Study in Mersin, Türkiye

Abstract

Abstract This study addresses the critical issue of earthquake vulnerability in Mersin, Türkiye, given its susceptibility to seismic threats due to factors such as high population density, substandard constructions, narrow roads, and urban congestion. The research employs a comprehensive approach, utilizing a multi-criteria evaluation model and a novel hybrid random forest model to estimate the city's vulnerability proportionally. Spatial data encompassing physical, population, building quality, accessibility, relief, and hazard facilities were incorporated into the assessment. Weights for these components were determined through the Analytic Network Process (ANP) model, and a hybrid approach using Linear, Small, and Large functions calculated distances between options with fuzzy-fication. Resampling 10m x 10m maps addressed variations in spatial resolutions, while an 80% training set and 20% test set mitigated overfitting concerns. Expert opinions were pivotal in establishing criteria and sub-criteria for determining safe areas for temporary accommodation, rescue centers, and a seismic vulnerability map. The Smile Random Forest hybrid model was instrumental in generating these outcomes. Notably, the vulnerability map indicated that 24% of Mersin's areas fall within the high and very high vulnerability range. Key contributors to vulnerability included Geology factors (26.4), Land use (16.1), Epicenters (13.1), and slope and DEM (6.4). These findings underscore the imperative for strategic planning and interventions to minimize earthquake-induced damage in Mersin.