Integrating interpretable artificial neural networks, geomorphic plausibility and transfer learning for landslide susceptibility mapping: a case study of Pacitan, East Java

Landslides are recurring natural hazards that continue to cause widespread damage to agricultural fields, vital infrastructures, and human lives globally. The impact is more severe in data-scarce regions where insufficient landslide inventory data limit accurate and timely landslide susceptibility assessment. This study integrates artificial neural networks, transfer learning, interpretable machine learning, and geomorphic plausibility to address the challenge of accurate landslide susceptibility mapping in data-scarce regions. Using the Pacitan regency as a case study, we assessed landslide susceptibility via artificial neural networks and transfer learning models. A neural network was first trained from a source area with a large dataset and then transferred through fine-tuning to another area with limited inventory data for mapping landslide susceptibility. A baseline model trained only on the limited target area data achieved an AUC test score of 0.83, the source-trained model had 0.88, while the transfer learning-based model achieved an AUC score of 0.97, confirming the improved performance and strong potential for transferability. Model interpretation using SHAP values, permutation importance, interaction strength, and partial dependence plots revealed slope, elevation, aspect, and distance to the stream as the most influential, while curvature and terrain indices were less impactful directly but contributed through interactions with other features. A qualitative assessment of geomorphic plausibility showed that the susceptibility patterns generally matched the behaviour of the terrain. Zones of high susceptibility were concentrated in the mid-slopes and at concave-convex transitions. Low susceptibility is observed in the steep rocky slope (> 40 °). These findings highlight the potential of interpretable machine learning, transfer learning, and geomorphic plausibility to produce physically meaningful susceptibility maps. This approach may be applied globally to produce rapid, interpretable, and generalisable landslide susceptibility maps in data-constrained regions, thereby contributing to more effective disaster mitigation and spatial planning.