Sensitivity of model-based leakage localisation in water distribution networks to water demand sampling rates and spatio-temporal data gaps

Abstract

Model-based leakage localisation in water distribution networks requires accurate estimates of nodal demands to correctly simulate hydraulic conditions. While digital water meters installed at household premises can be used to provide high-resolution information on water demands, questions arise regarding the necessary temporal resolution of water demand data for effective leak localisation. In addition, how do temporal and spatial data gaps affect leak localisation performance? To address these research gaps, a real-world water distribution network is first extended with the stochastic water end-use model PySIMDEUM. Then, more than 700 scenarios for leak localisation assessment characterised by different water demand sampling resolutions, data gap rates, leak size, time of day for analysis, and data imputation methods are investigated. Numerical results indicate that during periods with high/peak demand, a fine temporal resolution (e.g., 15 min or lower) is required for the successful localisation of leakages. However, regardless of the sampling frequency, leak localisation with a sensitivity analysis achieves a good performance during periods with low water demand (localisation success is on average 95%). Moreover, improvements in leakage localisation might occur depending on the data imputation method selected for data gap management, as they can mitigate random/sudden temporal and spatial fluctuations of water demands.