Details

Autor(en) / Beteiligte

• Mohammadi, Babak
• Linh, Nguyen Thi Thuy
• Pham, Quoc Bao
• Ahmed, Ali Najah
• Vojteková, Jana
• Guan, Yiqing
• Abba, S.I.
• El-Shafie, Ahmed

Titel

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

Ist Teil von

• Hydrological sciences journal, 2020-07, Vol.65 (10), p.1738-1751

Ort / Verlag

Abingdon: Taylor & Francis

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Taylor & Francis

Beschreibungen/Notizen

• 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.