For the 24-h prediction, the GRUN also achieves more precise forecasts with MAPE between 4.33% and 4.96%. Another study used three ML models to forecast both short-term and long-term water demand encompassing daily, weekly, and monthly intervals in Iran . These models include a dynamic artificial neural network (DAN2), a focused time-delay neural network (FTDNN), and a KNN. Given its inherent design of adjusting dynamically to the data-driven learning, DAN2 is promising at time-series forecasting, catering to datasets characterized by evolving temporal patterns. Correspondingly, DAN2 achieves remarkable prediction accuracies (96% for daily, 99% for weekly, and 98% for monthly water demand forecasts) and outperforms FTDNN and KNN.