  Landslide susceptibility mapping (LSM) is crucial for identifying high-risk
areas and informing prevention strategies. This study investigates the
interpretability of statistical, machine learning (ML), and deep learning (DL)
models in predicting landslide susceptibility. This is achieved by
incorporating various relevant interpretation methods and two types of input
factors: a comprehensive set of 19 contributing factors that are statistically
relevant to landslides, as well as a dedicated set of 9 triggering factors
directly associated with triggering landslides. Given that model performance is
a crucial metric in LSM, our investigations into interpretability naturally
involve assessing and comparing LSM accuracy across different models
considered. In our investigation, the convolutional neural network model
achieved the highest accuracy (0.8447 with 19 factors; 0.8048 with 9 factors),
while Extreme Gradient Boosting and Support Vector Machine also demonstrated
strong predictive capabilities, outperforming conventional statistical models.
These findings indicate that DL and sophisticated ML algorithms can effectively
capture the complex relationships between input factors and landslide
occurrence. However, the interpretability of predictions varied among different
models, particularly when using the broader set of 19 contributing factors.
Explanation methods like SHAP, LIME, and DeepLIFT also led to variations in
interpretation results. Using a comprehensive set of 19 contributing factors
improved prediction accuracy but introduced complexities and inconsistency in
model interpretations. Focusing on a dedicated set of 9 triggering factors
sacrificed some predictive power but enhanced interpretability, as evidenced by
more consistent key factors identified across various models and alignment with
the findings of field investigation reports....
