Principal Component Based Sampling for the Continuous Maintenance of Hydraulic Models

•Accurate models are needed for the digitalisation and control of water systems•Machine learning and optimization methods are combined with physical models•PCA-based sampling identifies beneficial hydraulic states for model fitting•Batches of data of any size can be considered for updating the hydraulic model•Three months of recorded hydraulic data confirms temporal predictive improvement [Display omitted] The calibration and continuous maintenance of hydraulic models is essential for the optimisation, planning and management of water distribution networks (WDNs). This process requires model fitting against multiple hydraulic states. However, when extended time series of hydraulic data are considered, it is essential to know which hydraulic states to use for model fitting as too many become computationally impractical. This paper presents a novel principal component analysis (PCA) based sampling method, which evaluates the significance of newly observed hydraulic data to be included in the model calibration and maintenance. A framework is presented, which allows for different sized batches of hydraulic data to be utilised. This enables both model calibration on extended time series of data, or model calibration and maintenance with continuous data. An extensive experimental program was conducted to investigate the performance of the proposed framework with different sampling methods compared to other conventional approaches. The results demonstrate that the presented sampling methods can maintain and improve model prediction accuracy both for retrospective and concurrent implementations when used with extended time series data, which capture different hydraulic states. The hydraulic model of the operational WDN used in this study and the acquired hydraulic data are provided as supplementary data to facilitate reproducible research.