GIS-Based Machine Learning and Deep Learning Approaches for Wastewater Failure Risk Classification and Prioritization: The Case of Kayseri Province

This study analyzed the classification of wastewater failures based on their risk levels in Kayseri’s central districts, utilizing citizen reports submitted to the Kayseri Water and Sewerage Administration. In this classification, Class 0 indicates low-priority failures requiring routine maintenance, whereas Class 1 represents high-priority failures necessitating emergency intervention. To evaluate failure risks, machine learning and deep learning methods, including XGBoost, LightGBM, Wide & Deep, TabTransformer, and SVM, were systematically applied. Among traditional machine learning techniques, XGBoost demonstrated competitive performance among the traditional machine learning models (Accuracy: 0.8833, F1-Score: 0.8806, AUC-ROC: 0.9269). Meanwhile, Wide & Deep achieved the highest Accuracy (0.8917) and F1-Score (0.8894), whereas DCN-v2 achieved the highest AUC-ROC (0.9251). Additionally, Mutual Information and 2D Kernel Density Estimation (2D KDE) analyses revealed localized spatial concentrations in the geographic data. Although these spatial features appear weakly related in standard correlation tests, they provide meaningful spatial information for failure risk classification. Among explainable AI approaches, SHAP analysis has been integrated into the models, and the stability of model performance was assessed using cross-validation metrics. The resulting classification framework may support workforce planning, maintenance prioritization, and operational decision-making in wastewater systems.