Amidst global scarcity, preventing pipeline failures in water distribution systems is crucial for maintaining a clean supply while conserving water resources. Numerous studies have modelled water pipeline deterioration; however, existing literature does not correctly understand the failure time prediction for individual water pipelines. Existing time-to-failure prediction models rely on available data, failing to provide insight into factors affecting a pipeline's remaining age until a break or leak occurs. The study systematically reviews factors influencing time-to-failure, prioritizes them using a magnitude-based fuzzy analytical hierarchy process, and compares results with expert opinion using an in-person Delphi survey. The final pipe-related prioritized failure factors include pipe geometry, material type, operating pressure, pipe age, failure history, pipeline installation, internal pressure, earth and traffic loads. The prioritized environment-related factors include soil properties, water quality, extreme weather events, temperature, and precipitation. Overall, this prioritization can assist practitioners and researchers in selecting features for time-based deterioration modelling. Effective time-to-failure deterioration modelling of water pipelines can create a more sustainable water infrastructure management protocol, enhancing decision-making for repair and rehabilitation. Such a system can significantly reduce non-revenue water and mitigate the socio-environmental impacts of pipeline ageing and damage.

The identification of areas prone to soil erosion in ungauged river basins is crucial for timely preventive measures, as erosion causes significant damage by lowering soil productivity and filling reservoirs with sedimentation. This study proposes a novel approach to prioritize sub-watersheds (SWs) in Ponnaniyar river basin. It utilizes different combinations of five objective-based weighting methods and seven Multi-criteria Decision Making (MCDM) techniques under outranking and synthesis methods with soil loss, morphometry, land use/land cover (LULC), and topography parameters. The results obtained from different hybrid models are validated using metrics like percentage and intensity of change. The findings reveal that MW-PROMETHEE (53.85%) and CRITIC-WASPAS (8.31) perform best in prioritizing areas based on morphometry, while CRITIC-TOPSIS (48.35% and 7.58) is more effective in prioritizing areas based on land use/land cover (LULC) and topography. The grade average method is used to integrate the rankings from 71 models: 35 based on morphometry, 35 based on LULC, and 1 based on the RUSLE model. The analysis identifies SW2 with a grade value of 4.34 as severely affected by soil erosion, followed by SW11 (5.45), SW5 (5.56), and SW9 (5.68), all falling within the very high priority level. This study recommends implementing appropriate water harvesting structures, which might be helpful in mitigating soil degradation, promoting soil conservation, and ensuring sustainable agricultural productivity.