@article{4720,
  author = {Pit Pichappan},
  title = {Global Airport Disruption Risk Assessment: A Data-Driven Analysis of Operational Vulnerabilities during the 2026 US-Iran War Using the Disruption Impact Index and K-means Clustering},
  journal = {Journal of E-Technology},
  year = {2026},
  volume = {17},
  number = {2},
  doi = {https://doi.org/10.6025/jet/2026/17/2/59-79},
  url = {https://www.dline.info/jet/fulltext/v17n2/jetv17n2_2.pdf},
  abstract = {This study presents a data-driven assessment of global airport disruption risks, analysing 76 disruption
events across 26 airports spanning five global regions: the Middle East, South Asia, Europe, the Asia Pacific,
and North Africa. This study is based on empirical data on airport disruptions collected during the 2026 USIran
war. A novel Disruption Impact Index (DII) is introduced to quantify operational consequences by
integrating three core variables: severity level (scored 1–5), disruption duration (in hours), and the number
of flights affected. Airport-level risk scores are computed by aggregating DII values across all recorded
events at each location. The methodology employs descriptive statistics, correlation analysis, Principal
Component Analysis (PCA), and K-means clustering (with optimal k=4 determined via Elbow Method and
validated by Silhouette Score) to identify structural patterns in disruption vulnerability. Results reveal that
Middle Eastern airports account for approximately 80% of total disruption risk, with Abu Dhabi (AUH),
Kuwait (KWI), and Baghdad (BGW) ranking highest. The number of affected flights shows the strongest
correlation with disruption impact (r = 0.86), followed by severity level (r = 0.72) and duration (r = 0.69).
Four distinct airport clusters were identified, ranging from high risk Gulf hubs vulnerable to fuel shortages
and security issues to lower-risk regional airports experiencing operational delays. PCA confirmed that the
first two principal components explain 78–82% of total variance, validating the multidimensional nature of
disruption risk. These findings support evidence-based decision making for strengthening network wide
resilience, recommending priority monitoring for critical airports, fuel reserve protocols for Gulf regions,
and strategic utilization of lower risk airports as diversion hubs during regional crises.},
}