Short-term (e.g., hourly) urban water consumption (or demand) prediction is of great significance for the optimal operation of the intelligent water distribution pump stations. In this study, three single models (autoregressive integrated moving average (ARIMA), back-propagation (BP), support vector machine (SVM)) and three hybrid models (ensemble empirical mode decomposition (EEMD)-ARIMA, EEMD-BP and EEMD-SVM) were developed and compared in terms of prediction accuracy and application convenience. 31-day (1 month) hourly flow series from a water distribution division in Shanghai were used for the demonstration case study, among which 30-day data used for model training and 1-day data used for model verification. Finally, the effects of historical data length on the prediction accuracy of three hybrid models were also analyzed, and the optima of the historical data length for three hybrid models were obtained. Results reveal that (1) the mean absolute percentage errors (MAPE) of EEMD-ARIMA, EEMD-BP, EEMD-SVM, ARIMA, BP and SVM are 5.2036, 1.4460, 1.3424, 5.7891, 4.3857 and 3.8470%, respectively. (2) In terms of prediction accuracy and actual practice convenience, EEMD-SVM performs best among the above six models. (3) The EEMD algorithm is effective for improving the prediction accuracy of six models. (4) The optimal historical data length of EEMD-ARIMA, EEMD-BP and EEMD-SVM are 11, 11 and 10 days, respectively.

短期（例如，每小时）城市用水量（或需求）预测对于智能配水泵站的优化运行具有重要意义。在这项研究中，三个单一模型（自回归集成移动平均（ARIMA）、反向传播（BP）、支持向量机（SVM））和三个混合模型（集成经验模态分解（EEMD）-ARIMA、EEMD-BP 和 EEMD -SVM) 在预测准确性和应用便利性方面进行了开发和比较。示范案例研究使用上海某配水部门的 31 天（1 个月）小时流量序列，其中 30 天数据用于模型训练，1 天数据用于模型验证。最后还分析了历史数据长度对三种混合模型预测精度的影响，得到了三种混合模型的历史数据长度的最优值。结果表明：（1）EEMD-ARIMA、EEMD-BP、EEMD-SVM、ARIMA、BP和SVM的平均绝对误差百分比（MAPE）分别为5.2036、1.4460、1.3424、5.7891、4.3857和3.8470%。(2)在预测精度和实际实践便利性方面，EEMD-SVM在上述六种模型中表现最好。(3)EEMD算法对提高六种模型的预测精度是有效的。(4)EEMD-ARIMA、EEMD-BP和EEMD-SVM的最佳历史数据长度分别为11、11和10天。(2)在预测精度和实际实践便利性方面，EEMD-SVM在上述六种模型中表现最好。(3)EEMD算法对提高六种模型的预测精度是有效的。(4)EEMD-ARIMA、EEMD-BP和EEMD-SVM的最佳历史数据长度分别为11、11和10天。(2)在预测精度和实际实践便利性方面，EEMD-SVM在上述六种模型中表现最好。(3)EEMD算法对提高六种模型的预测精度是有效的。(4)EEMD-ARIMA、EEMD-BP和EEMD-SVM的最佳历史数据长度分别为11、11和10天。