Accurate streamflow (Q_t) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering and scientific fields, and their application has increased in the last few years. In this study, the predictive capabilities of the reduced error pruning tree (REPT) model, used both as a standalone model and within five ensemble-approaches, were evaluated to predict streamflow in the Kurkursar basin in Iran. The ensemble-approaches combined the REPT model with the bootstrap aggregation (BA), random committee (RC), random subspace (RS), additive regression (AR) and disjoint aggregating (DA) (i.e. BA-REPT, RC-REPT, RS-REPT, AR-REPT and DA-REPT). The models were developed using 15 years of daily rainfall and streamflow data for the period 23 September 1997 to 22 September 2012. A set of eight different input scenarios was constructed using different combinations of the input variables to find the most effective scenario based on the linear correlation coefficient. A comprehensive suite of graphical (time-variation graph, scatter-plot, violin plot and Taylor diagram) and quantitative metrics (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliff efficiency (NSE), Percent of BIAS (PBIAS) and the ratio of RMSE to the standard deviation of observation (RSR)) was applied to evaluate the prediction accuracy of the six models developed. The outcomes indicated that all models performed well but the AR-REPT outperformed all the other models by rendering lower errors and higher precision across a number of statistical measures. The use of the BA, RC, RS, AR and DA models enhanced the performance of the standalone REPT model by about 26.82%, 18.91%, 7.69%, 28.99% and 28.05% respectively.

准确的流量（Q _t) 预测可为城市水文管理策略提供关键信息，例如防洪、长期水资源管理、土地利用规划以及农业和灌溉操作。自 20 世纪中叶以来，人工智能 (AI) 模型已广泛应用于工程和科学领域，并且其应用在最近几年有所增加。在这项研究中，评估了减少误差修剪树 (REPT) 模型的预测能力，该模型用作独立模型和五个集合方法，用于预测伊朗库尔库萨尔盆地的水流。集成方法将 REPT 模型与引导聚合 (BA)、随机委员会 (RC)、随机子空间 (RS)、加性回归 (AR) 和不相交聚合 (DA)（即 BA-REPT、RC-REPT、RS -REPT，AR-REPT 和 DA-REPT）。这些模型是使用 1997 年 9 月 23 日至 2012 年 9 月 22 日期间 15 年的日降雨量和流量数据开发的。使用输入变量的不同组合构建了一组八个不同的输入场景，以基于线性相关系数。一套全面的图形（时变图、散点图、小提琴图和泰勒图）和定量指标（均方根误差 (RMSE)、平均绝对误差 (MAE)、纳什-萨特克利夫效率 (NSE)、百分比BIAS (PBIAS) 和 RMSE 与观测标准差 (RSR) 的比值用于评估开发的六个模型的预测精度。结果表明，所有模型都表现良好，但 AR-REPT 通过在许多统计测量中呈现更低的错误和更高的精度而优于所有其他模型。BA、RC、RS、AR 和 DA 模型的使用将独立 REPT 模型的性能分别提高了约 26.82%、18.91%、7.69%、28.99% 和 28.05%。