Leakage in water distribution systems (WDS) is a major problem that results in substantial resource wastage. Pressure management, which is based on optimized control strategies using pressure-reducing valve installation, is considered one of the most effective methods for controlling network leakage and can be broadly classified into varied types, depending on the different control strategies. When facing diverse operational conditions in actual engineering, selecting the most suitable optimization strategy for the specific water supply network can greatly control leakage and efficiently utilize water resources. This article employs a greedy algorithm to iteratively determine the control sequence for pressure-reducing valves (PRVs), with the objective of minimizing surplus pressure. Subsequently, it proposed five evaluation indicators, namely, daily flow, daily leakage, pressure imbalance indicator, median daily pressure at nodes, and water age, which are used to evaluate and compare pressure management strategies in two sample networks. Finally, a suitable control strategy was successfully developed and implemented for an actual WDS of Y city in southern China, resulting in significant achievements. In conclusion, this paper embodies our attempt and contemplation in formulating pressure management strategies under diverse operating conditions in WDS, with the objective of providing guidance for practical engineering applications.

Machine learning’s application in short-term water demand forecasting remains a pivotal area of research in water distribution system studies. This investigation reveals a distinctive distribution pattern for the daily demand following dataset preprocessing with Random Forest and the quartile method. Inspired by the findings, this study introduces a novel Water Demand Forecast Framework (WDFF) using DMA characteristics and the CNN–Attention–LSTM architecture. By analyzing the relationship between the total and DMA-specific demand, the WDFF is found to enhance the predictions. It demonstrates expedited convergence and reduces the loss metric, demonstrating its potential to elevate the predictive precision in water demand forecasting.