Forecasting short-term water demands is one of the most critical needs of operating companies of urban water distribution networks. Water demands have a time series nature, and various factors affect their variations and patterns, which make it difficult to forecast. In this study, we first implemented a hybrid model of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to forecast urban water demand. These models include a combination of CNN with simple RNN (CNN-Simple RNN), CNN with the gate recurrent unit (CNN-GRU), and CNN with the long short-term memory (CNN-LSTM). Then, we increased the number of CNN channels to achieve higher accuracy. The accuracy of the models increased with the number of CNN channels up to four. The evaluation metrics show that the CNN-GRU model is superior to other models. Ultimately, the four-channel CNN-GRU model demonstrated the highest accuracy, achieving a mean absolute percentage error (MAPE) of 1.65% for a 24-h forecasting horizon. The effects of the forecast horizon on the accuracy of the results were also investigated. The results show that the MAPE for a 1-h forecast horizon is 1.06% in four-channel CNN-GRU, and its value decreases with the amount of the forecast horizon.

This study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances forecasting accuracy and reliability. This integration not only leverages the unique strengths of each method but also compensates for their individual weaknesses, resulting in a robust solution for predicting urban water demand. Tested against the Battle of Water Demand Forecasting dataset (WDSA-CCWI-2024), our ensemble model demonstrates superior performance, offering a promising tool for efficient water resource management and decision making.