Rockfalls are one of the most significant geohazards that endanger transportation infrastructures in the mountainous region of Kurdistan Region of Iraq, particularly along the Barzan Highway within Duhok City. This study introduces a high-resolution, multi-parametric Rockfall Susceptibility Assessment (RSA) applied to the delineation of hazardous zones along this important road transportation network. The AHP-based framework significantly enhances the accuracy and decision reliability of rockfall susceptibility assessment by incorporating expert-driven weighting of ten conditioning factors. Via Geographic Information System (GIS) based AHP, the study incorporates ten conditioning factors obtained from sophisticated remote sensing data. A sub-meter Digital Surface Model (DSM) was built by mobile LiDAR Scanning (Stonex X120Go) to characterize the micro-relief and to upscale structural discontinuities detection. The Land Use/Land Cover (LULC) was also classified with a sequence CNN model using the Sentinel-2 satellite data, with an overall accuracy of 97.40%. The AHP analysis, verified by expert opinions with a Consistency Formula: see text, revealed that slope (37.29%) and structural discontinuities (11.71%) were the most significant contributors to instability. The resulting susceptibility map shows that 26.31% of the corridor is classified into High to Very High susceptibility zones, predominantly along the steep limestone cuts of the western ridge. A longitudinal profile analysis, supported by Theis solution, establishes a clear relation between man-made excavation and hazard maxima, with the maximum susceptibility index (0.40) observed in vertical cut sections. These findings highlight the efficacy of integrating deep learning and mobile LiDAR within the AHP framework, offering local authorities an accurate, station-level zonation map for targeted mitigation efforts.
Awni et al. (Wed,) studied this question.