Prediction of water production and demand is critical in safe drinking water supply. A hybrid model that combines genetic algorithms (GA) and GA artificial neural networks (GA-ANN) can predict drinking water production (Figure ) . The model uses temperature, COD, and electricity and chemical consumption as the inputs. The GA-ANN was trained and validated with monthly data from 45 water utilities across China. The R 2 (0.93) of GA-ANN is substantially higher than that of the ANN (0.71) when more training data are incorporated. GA not only optimizes the weights and biases to enhance prediction accuracy but also increases the tolerance to imprecision, uncertainties, and approximates in the inputs. Moreover, GA-ANN could effectively forecast fluctuations in water production for various scenarios, highlighting its feasibility in adjusting water treatment operations. To assess drinking water demand patterns, one can apply unsupervised learning algorithms (such as the hierarchical K-means algorithm) to raw time-series data of drinking water consumption . In that algorithm, daily timeseries demand with hourly records are the inputs within a 24-dimensional feature space to identify dense and distinctly separated temporal patterns of water demand. In contrast to directly clustering the water demand patterns, short-time water demand forecasting is more intriguing and has more practical merits in optimizing drinking water supply. A gated recurrent unit network (GRUN) predicts short-term water demand for different district metering areas for the upcoming 15 min and 24 h using a time-step of 15 min . The GRUN has a higher accuracy with a lower mean absolute percentage error (MAPE) between 2.06% and 2.46% than the conventional three- dense-layered ANN (MAPE between 2.46% and 2.54%) and the seasonal autoregressive integrated moving average (SARIMA) model (MAPE between 2.57% and 2.85%) for the 15-min prediction.