20.500.12556/RUL-185607 AI-driven risk management for sustainable water distribution a comparative study of resampling strategies and cost-sensitive predictive modeling for leakage failure Aging water infrastructure and the resulting increase in pipe leaks pose significant operational and financial challenges for modern utilities, requiring more accurate tools for failure identification. This study presents a comprehensive benchmarking framework designed to predict pipe failure probability by evaluating a wide array of state-of-the-art classification models, including traditional baselines, tree-based ensembles, and emerging tabular deep learning architectures. The methodology integrates high-resolution datasets with a dedicated evaluation of spatially derived infrastructure indicators to capture the complex environmental and physical drivers of failure. To address inherent class imbalance, the study systematically benchmarks resampling strategies, such as SMOTE, ADASYN, and RUS, to determine whether these techniques truly improve decision-making performance. This assessment is grounded in the application of proper scoring rules, specifically Logarithmic Loss and Brier Score, alongside the introduction of the Area Under the Cost Curve to evaluate the economic implications of predictive performance across varying cost scenarios. water distribution networks leakage failure machine learning resampling vodovodna omrežja okvare zaradi puščanja strojno učenje prevzorčenje true false true Angleški jezik Slovenski jezik Članek v reviji 2026-08-12 14:15:55 2026-08-12 14:16:08 2026-08-13 04:00:39 0000-00-00 00:00:00 2026 0 0 9 str. Vol. 306, art. 126622 Nov. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 626/627:004.8 1879-2448 10.1016/j.watres.2026.126622 287624963 Abert...-AI-driven_risk_management_for_sustainable_water_distribution.pdf Abert...-AI-driven_risk_management_for_sustainable_water_distribution.pdf 1 F645AFD0E230B1999B71AC1AA7EDB3E0 a0f6883e9e89be4d3325f927c50a4c9e4fc76a3b16c9fc7880b9c3c56501637d 96b7ce8e-9647-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244311 https://www.sciencedirect.com/science/article/pii/S0043135426012960 1 3fcb1035-9647-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=244310 Fakulteta za gradbeništvo in geodezijo 0 0 0