Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Issue 10 (26th July 2020)

• Record Type: Journal Article
• Authors: Mohammadi, Babak; Linh, Nguyen Thi Thuy; Pham, Quoc Bao; Ahmed, Ali Najah; Vojteková, Jana; Guan, Yiqing; Abba, S.I.; El-Shafie, Ahmed
• Is Part Of: Hydrological sciences journal. Volume 65:Issue 10(2020)
• Page Start: 1738
• Page End: 1751
• Publication Date: 2020-07-26
• Subjects: Streamflow -- estimation -- time series models -- adaptive neuro-fuzzy inference system (ANFIS) -- shuffled frog leaping algorithm (SFLA); Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2020.1758703
• Languages: English
• ISSNs: 0262-6667

Abstract

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 (R 2 = 0.88; NS = 0.88; RMSE = 142.30 (m 3 /s); MAE = 88.94 (m 3 /s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R 2 = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m 3 /s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.