| 20 May 2026
Unraveling Spatial Dependencies in Landslide Susceptibility using Directed Acyclic Graphs
Abstract. Data-driven methods for landslide susceptibility assessment (LSA) often suffer from spurious correlations and “black-box” opacity, failing to capture the spatial dependency processes underlying landslide development. To address these limitations, we propose a directed acyclic graph (DAG)-informed interpretable framework by integrating structure-learning algorithms and graph attention models. This approach enables the identification of spatial dependency pathways and quantifies the propagation magnitudes (weights of connected links) of landslide conditioning factors. We applied this framework to the Ili River Basin, Xinjiang, China. A total of 14 robust spatial dependency chains were identified, and the dominant susceptibility-related chains were categorized into four types: (1) Elevation–climate-driven pathways (Elevation → Precipitation → NDWI → Landslide; Elevation → Precipitation → Temperature → Snow Depth → NDWI → Landslide); (2) Tectonic-controlled pathways (Distance to faults → PGA → Landslide); (3) Topographic dominated pathways (Slope → Curvature → Landslide); and (4) Hydrological driven pathways (Distance to rivers → NDWI → Landslide). Using a novel importance-weighted decoupling method, we generated pathway-specific susceptibility maps. These four chains account for 18.32%, 15.74%, 17.67%, and 16.76% of the high-susceptibility areas, respectively. These areas are predominantly clustered in mid–high mountainous, high-intensity seismic, and weakened lithological belt regions. Our proposed framework advances LSA from statistical prediction to dependency-informed explanation, providing decision-makers with a scientific basis for interpreting susceptibility variations across different spatial and environmental settings.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
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The manuscript presents a DAG-guided explainable framework for landslide susceptibility assessment; however, the overall scientific quality and practical contribution remain insufficient for publication in its current form. A major concern is the low quality and limited representativeness of the fundamental dataset. The landslide inventory contains only 1,198 landslides over a very large and geologically complex region, which is inadequate to support the proposed highly parameterized DAG-GNN framework and the claimed discovery of “robust dependency pathways.” The manuscript lacks sufficient information regarding inventory completeness, temporal consistency, mapping uncertainty, validation strategy, and the balance between landslide and non-landslide samples. Many environmental factors are derived from coarse-resolution or secondary datasets resampled to 30 m resolution, yet the impacts of spatial uncertainty and scale effects are not rigorously evaluated. In addition, the proposed causal interpretations appear largely speculative and are not adequately supported by physical evidence or independent validation. Several “dependency pathways” merely reflect common statistical associations among topography, precipitation, and hydrological indicators rather than revealing genuinely new scientific mechanisms. The manuscript repeatedly emphasizes explainability and causal inference, but the DAG structure is strongly dependent on expert intervention and manual correction, which substantially weakens the objectivity and reproducibility of the framework. Moreover, the scientific novelty is overstated because similar graph-based or explainable AI approaches for landslide susceptibility assessment have already been widely explored in recent years. The manuscript does not convincingly demonstrate a substantial methodological breakthrough or significant improvement over existing approaches. The expression and presentation quality are also poor. The manuscript contains numerous grammatical problems, unclear logical transitions, repetitive descriptions, and overinterpretation of results. Several figures are visually cluttered and difficult to interpret, while the discussion section remains descriptive and lacks deep scientific analysis regarding uncertainty, transferability, and physical implications. Overall, the manuscript suffers from insufficient data reliability, limited scientific advancement, weak mechanism validation, and poor presentation quality. Therefore, I do not recommend publication, and rejection is suggested.