Leveraging Principal Component Analysis for Data-Driven and Objective Weight Assignment in Spatial Decision-Making Framework for Qanat-Induced Subsidence Susceptibility Assessment in Railway Networks

Railway networks are highly susceptible to land subsidence, which can undermine their functional stability and safety, resulting in recurring failures and vulnerabilities. This paper aims to evaluate the susceptibility of the railway network due to Qanat underground channels in the city of Bafq, Iran. The criteria considered for assessing the susceptibility of Qanats subsidence on the railway network in this study are Qanat channel density, Qanat well density, discharge rate of the Qanat, depth of the Qanat, railway traffic, and the railway passing load. The subjective determination of criteria weights in Multi-Criteria Decision-Making (MCDM) for susceptibility analysis is typically a complex, time-consuming, and biased task. Furthermore, there is no comprehensive study on the impact and relative significance of Qanat-related factors on railway subsidence in Iran. To address this gap, this study developed a novel spatial objective weighting approach based on Principal Component Analysis (PCA)—as an unsupervised Machine Learning (ML) technique—within a spatial decision-making framework specifically designed for railway susceptibility assessment. In the proposed framework, the final Qanat-induced subsidence susceptibility zoning was conducted using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This study identified 7.7 km2 of the total area as a high-susceptibility zone, which encompasses 15 km of railway network requiring urgent attention. The developed framework demonstrated promising performance without deploying subjective information, providing a robust data-driven approach for susceptibility assessment in the study area.