Machine-Learning Modelling of Normalized Water-Pipe Failure Rates Using Random Forest and XGBoost: A Clustered Case Study from Malatya, Türkiye

Water-distribution-system failure records often contain nonlinear, heterogeneous patterns that are difficult to summarize with conventional statistics alone. This study evaluates how Random Forest and XGBoost reconstruct normalized water-pipe failure-rate patterns in a field-derived Malatya, Türkiye dataset and uses distributional diagnostics, empirical ranking, and multi-method explainability to identify stable model drivers. The analysis includes 1231 records from nine source-defined clusters and four modelling inputs: cluster identifier, normalized pipe length, normalized pipe diameter, and normalized second-failure age. Because the models were fitted to and evaluated on the same records, the reported performance represents apparent/full-data goodness of fit rather than independent predictive validation. XGBoost provided the closer reconstruction (R2 = 0.9189 versus 0.8523 for Random Forest; MAE = 0.0230 versus 0.0321). Across permutation importance, SHAP attribution, and variable-removal sensitivity, pipe length and second-failure age emerged as the most robust model-relevant features, whereas native importance was more method-dependent. The framework demonstrates how ensemble models can support transparent retrospective database screening and pattern interpretation while maintaining a clear boundary between historical fit and forecasting. Temporal, spatial, pipe-grouped, or external validation is required before prospective decision use.