Data‐Driven Insights Into Post‐Earthquake Reconnaissance Findings: 2023 Türkiye Earthquake Sequence

Post‐earthquake reconnaissance stands as a vital endeavor, encompassing a systematic survey and data collection to gain insights into the aftermath of seismic events. Grappling with a multitude of multidimensional data poses inherent challenges in the proper interpretation and extraction of hidden information. This paper unfolds in two parts. Initially, we delineate the reconnaissance mission following the February 06 Türkiye earthquake sequence, elucidating the challenges, complexities, and nature of the amassed data. Subsequently, a paradigm shift toward automated machine learning (AutoML) is embraced to ascertain the optimal model, categorize observed damages, and unveil underlying patterns within the collected data. The most accurate machine learning model is then coupled with Shapley Additive Explanations (SHAP) to explicate observations, steering away from a black‐box model. SHAP values discern and prioritize factors significantly contributing to various damage levels. The results reveal a test set accuracy of 0.75 and 0.95 for multi‐ and binary‐class problems, respectively, employing both raw and composite features. Building‐related features exert more control over light damage, while earthquake‐related factors dominate severe damage. In critical damage scenarios, duration‐ and velocity‐dependent intensity measures assume significance. Furthermore, findings indicate that if column and wall indices are below 0.07 and 0.08, respectively, they positively contribute to the likelihood of critical structural damage.