Accurate estimating of daily streamflow forecasting is one of the prominent topics in water resources activities. In this paper, an integrated method including decomposition technique based on the ensemble empirical mode decomposition (EEMD) combined with multivariate adaptive regression spline (MARS) was carried out to predict daily streamflow values. Daily streamflow value datasets collected from two stations in Iran (Gachsar and Kordkheyl) were selected. After dividing into calibration and validation datasets, each of them was decomposed by EEMD. Crow search algorithm (CSA) was used to optimize the MARS parameters (MARS-CSA). The performance of the integrated model (EEMD-MARS-CSA) was investigated by error indices (correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), as well as RMSE to standard deviation ratio (RSR)). From the results, EEMD was an important tool for increasing model accuracy and EEMD-MARS-CSA outperformed other alternative methods for daily streamflow estimation. According to one-day-ahead flow forecasting, EEMD-MARS-CSA (R = 0.94, RMSE = 5.94 m³/s (Kordkheyl) and R = 0.98, RMSE = 0.71 m³/s (Gachsar)) outperformed EEMD-MT/MARS, MT, and MARS models. Furthermore, RSR criterion of EEMD-MARS-CSA was reduced by 18%, 16%, and 17% for 3-days, 1-week, and 2-weeks-ahead streamflow forecasting compared to MARS-CSA model, respectively, for Gachsar station.

每日流量预测的准确估算是水资源活动中的突出主题之一。本文采用综合经验模态分解（EEMD）和多元自适应回归样条（MARS）相结合的分解方法，对日流量进行了预测。选择了从伊朗两个加油站（Gachsar和Kordkheyl）收集的每日流量值数据集。分为校准和验证数据集后，每个数据集均由EEMD分解。乌鸦搜索算法（CSA）用于优化MARS参数（MARS-CSA）。通过误差指数（相关系数（R），均方根误差（RMSE），平均绝对误差（MAE），纳什–萨特克利夫效率（NSE），误差指数）研究了集成模型（EEMD-MARS-CSA）的性能。以及RMSE与标准差之比（RSR））。从结果来看，EEMD是提高模型准确度的重要工具，而EEMD-MARS-CSA的日常流量估算性能优于其他替代方法。根据提前一天的流量预测，EEMD-MARS-CSA（R = 0.94，RMSE = 5.94 m³ / s（Kordkheyl）和R = 0.98，RMSE = 0.71 m ³ / s（Gachsar））优于EEMD-MT / MARS，MT和MARS模型。此外，与GACHSAR的MARS-CSA模型相比，提前3天，1周和2周的流量预测EEMD-MARS-CSA的RSR标准分别降低了18％，16％和17％站。