# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 5 Conclusions  
This study comprehensively evaluated landslide prediction performance by comparing empirical rainfall threshold models with several machine learning algorithms. To enhance model transparency and interpretability, SHAP (Shapley Additive Explanations) analysis was employed to elucidate model decision-making processes. Building upon the optimal model, we developed visualized rainfall threshold maps for the Italian region under three probability scenarios. The key findings are as follows:  
1. The XGBoost model achieved superior overall performance (AUC = 0.917 \(\pm\) 0.026) with well-balanced sensitivity (0.792 \(\pm\) 0.075) and specificity (0.812 \(\pm\) 0.033), proving more suitable for modeling imbalanced datasets. The model significantly outperformed other machine learning approaches, thereby demonstrating its exceptional suitability for landslide modeling and early warning applications. 2. Total rainfall and rainfall intensity were the dominant triggering factors, far exceeding other factors in importance. SHAP analysis showed a pronounced increase in influence within the 30-50 mm range, with the maximum impact occurring beyond 50 mm. Rainfall intensity demonstrated critical thresholds above 21.5 mm/day, with peak influence beyond 47.9 mm/day. Among static environmental factors, elevation showed an inverse relationship with landslide probability, while proximity to rivers exhibited distance-dependent effects, with higher risk within 1500 m of waterways. 3. Regional differences in landslide-triggering rainfall thresholds were observed across Italy. Areas characterized by gentler terrain (slopes < 25\({}^{\circ}\)), such as Marche, Tuscany, and parts of Emilia-Romagna, along with moderate-rainfall regions including central Apennine areas and the Po Valley regions, demonstrated higher rainfall thresholds with values greater than 42 mm and 53 mm at 50% and 70% probability levels, respectively. In contrast, steeper slopes (>35\({}^{\circ}\)) found in regions such as Liguria, Umbria, and southern Calabria showed lower rainfall thresholds of less than 34 mm and 48 mm at the two probability levels, respectively. At the 90% probability level, thresholds universally increased, and regional disparities diminished. Overall, this study provides a viable approach for rainfall-induced landslide prediction and offers a practical reference for regional early warning systems. Future research should focus on reducing intra-regional errors and enhancing the cross-regional transferability of the developed models. The Python environment and core Python libraries used in this study are detailed at: [[https://www.mdpi.com/article/10.3390/app15147937/s1](https://www.mdpi.com/article/10.3390/app15147937/s1)]([https://www.mdpi.com/article/10.3390/app15147937/s1](https://www.mdpi.com/article/10.3390/app15147937/s1)). **Author Contributions:** Conceptualization, X.S. ([PERSON]) and W.Y. ; methodology, X.S. ([PERSON]) and C.Y. ; software, X.S. ([PERSON]) and J.Y. ; validation, X.S. ([PERSON]) and Y.W. ; investigation, X.S. ([PERSON]) and X.S. ([PERSON]); resources, X.S. ([PERSON]) and H.D. ; data curation, X.S. ([PERSON]) and T.L. ; visualization, X.S. ([PERSON]) and P.Z. ; supervision, Z.Z. ; writing--original draft preparation, X.S. ([PERSON]); writing--review and editing, W.Z. and J.J.; project administration, J.J.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the Science and Technology Projects of the Xizang Autonomous Region, China (No. XZ202402 ZD0001), the Basic Research Program of Qinghai Province (2024-ZJ-904), and the Key Program of Gansu Joint Scientific Research Fund (Grant No. 25 JRRA1106). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets generated and/or analyzed during this study are available from the first author ([PERSON]) upon reasonable request.