# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 4 Discussion
### Landslide-Triggering Factors  
This study used the XGBoost model with SHAP interpretation to identify which factors are most important for predicting rainfall-triggered landslides and understand how they work. Our results show that, in our temporal landslide prediction approach, dynamic weather and rainfall conditions are far more important than static environmental factors such as topography and geology. This pattern reflects the model's effectiveness in capturing essential information to distinguish between landslide and non-landslide events.  
Among all factors, total rainfall stands out as the most critical predictor, with SHAP values that are roughly ten times higher than other variables. Higher total rainfall consistently increases landslide probability, which matches our physical understanding of slope failure. When large amounts of rain fall over short periods, water infiltrates the soil, increases pore water pressure, and reduces the soil's ability to resist sliding. This combination of effects directly destabilizes slopes and triggers landslides [1]. While rainfall intensity also contributes significantly to landslide prediction, it has much lower importance than total rainfall. This suggests that the cumulative amount of water entering the soil matters more than how fast it falls at any given moment. This makes physical sense because landslides typically occur when water reaches deeper soil layers and potential failure surfaces, rather than being triggered by surface conditions alone. The process depends more on sustained water infiltration than on peak rainfall rates [59; 60].  
The limited influence of static environmental factors can be understood from two perspectives. From a physical standpoint, while topographic and geological features determine long-term landslide susceptibility through gradual geological processes [61], they become less influential during actual rainfall events when dynamic hydrological processes directly control slope stability. The rapid changes in soil moisture and pore water pressure during storms override the influence of relatively uniform geological conditions. From a methodological perspective, our training data come from historical landslide locations where static environmental factors have already reached critical values over geological time scales. Since these sites are already predisposed to failure, the static factors show limited variation and thus provide little discriminatory power in distinguishing between triggering and non-triggering events.  
This pattern also reflects a fundamental difference between our rainfall-event-based modeling approach and traditional landslide susceptibility mapping [62; 63]. Susceptibility maps identify where landslides are likely to occur based on long-term geological predisposition, using spatial sampling to compare stable and unstable areas. In contrast, our models focus on when landslides will occur by distinguishing between rainfall events that trigger landslides and those that do not, using temporal sampling to capture the critical timing of landslide initiation.