White-Boxed Landslide Susceptibility Mapping Using Unsupervised Causal Model

Currently in Landslide Susceptibility Mapping (LSM), data-driven models mostly utilize association to present the connection between landslide and Conditioning Factor (CF). However, association cannot reflect the structural interaction or the underlying causal mechanism. This leads to black-box results and reduces interpretability. To address this problem, this study introduces an unsupervised causal model to LSM. The methodology consists of two parts: causal discovery with Non-combinatoric Optimization via Trace Exponential Augmented Lagrangian Structure learning (NOTEARS) algorithm, and causal inference with Bayesian Networks (BN). By applying structural causality to LSM, we “white-boxed” the procession of data-driven model, thereby ensuring the interpretability and data utility. To comprehensively evaluate the results, comparison experiments with other machine learning models were performed. The results showed that BN achieved the highest accuracy (approximately 0.877) and the second highest AUC (0.907). Moreover, BN outperformed other models in ground-truth validation. The high and extremely high susceptibility zones in BN-LSM result covered the most actual landslides (more than 71.07% of the total, and more than 80% after excluding areas not used in model training). This showed the generalizability and stability of structural causality-guided-BN. The proposed methodology provides a link between data-driven causal science and natural hazard analysis, which can help intelligent hazard managements. Data and codes that support the findings of this study are openly available at doi.org/10.6084/m9.figshare.26395420.