Authors
Yousef Bahrami, Abbas Maghsoudi, Amin Beiranvand Pour, Masoud Shirali
Published in
Scientific reports. Jul 02, 2026. Epub Jul 02, 2026.
Abstract
Landslide susceptibility mapping in mountainous transportation corridors requires modelling approaches that capture complex nonlinear relationships and spatial dependencies among geo-environmental factors. This study proposes a hybrid graph-based artificial intelligence framework (GraphSAGE-CatBoost optimized by the Reptile Search Algorithm) for landslide susceptibility assessment along the Karaj-Chalus road, a critical transportation corridor in the central Alborz mountains, northern Iran. The framework explicitly models terrain units as nodes in a spatially structured graph, enabling the capture of topological dependencies that are inherently inaccessible to conventional pixel-based or tabular approaches. A comprehensive database comprising of 409 documented landslides, 409 non-landslide samples, and ten conditioning factors (lithology, distances to faults, roads and rivers, land use/land cover, rainfall, slope, aspect, topographic wetness index, and stream power index) was compiled for model development. To rigorously benchmark the proposed graph-based model against complementary state-of-the-art paradigms, two representative models were selected: (1) a GoogleNet-CNN optimized by Harris Hawks Optimization, representing deep convolutional approaches with multi-scale feature extraction; and (2) an Autoencoder-XGBoost hybrid, representing hybrid deep-ensemble strategies with unsupervised feature compression. Under random split validation (70/30), the GraphSAGE-CatBoost-RSA model achieved superior performance across multiple metrics: AUC-ROC = 0.972 (95% bootstrap CI [0.963-0.981]), AUC-PR = 0.962 ([0.951-0.973]), accuracy = 0.932, precision = 0.915, recall = 0.948, F1 = 0.931, and test error = 0.068, outperforming both benchmarks with non-overlapping confidence intervals. Importantly, spatial cross-validation using three geographically independent sub-regions confirmed that these results are not due to spatial leakage; the model maintained strong generalization (mean AUC-ROC = 0.924, mean accuracy = 0.886, mean F1 = 0.885), with only a 5% reduction from the random-split performance. Geomorphologically, high-susceptibility zones are strongly associated with steep slopes underlain by weak clay-rich lithologies, proximity to road cuts and drainage networks, and north-facing aspects where soil moisture persists. The resulting susceptibility map identifies priority segments for targeted mitigation, including tunnel portals, bridge abutments, and cut-slopes requiring drainage upgrades or stabilization. The proposed framework provides a spatially explicit and rigorously validated modelling approach for landslide susceptibility mapping in mountainous transportation corridors, offering a robust basis for comparing graph-based learning with established deep-learning and hybrid machine-learning alternatives.
PMID:
42393205
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 3
- Comments 0