ABSTRACT Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R2 = 0.88; NS = 0.88; RMSE = 142.30 (m3/s); MAE = 88.94 (m3/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R2 = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m3/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.

中文翻译：

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自适应神经模糊推理系统结合混洗蛙跳算法预测河流流量时间序列

摘要 准确的径流预测在集水区水资源管理和水资源系统规划中起着关键作用。为提高预测精度，需要努力开发一种可靠、准确的径流预测模型。在这项研究中，提出了自适应神经模糊推理系统（ANFIS）模型与混洗蛙跳算法（SFLA）的新组合。收集了两条不同河流的历史流量数据以检查所提出模型的性能。为了评估所提出的 ANFIS-SFLA 模型的性能，研究了模型输入-输出架构的六种不同场景。结果表明，提出的ANFIS-SFLA模型（R2=0.88；NS=0.88；RMSE=142.30（m3/s）；MAE=88.94（m3/s）；MAPE=35。19%) 显着提高了预测精度并优于经典 ANFIS 模型（R2 = 0.83；NS = 0.83；RMSE = 167.81；MAE = 115.83 (m3/s)；MAPE = 45.97%）。所提出的模型可以推广并应用于全球不同的河流。