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

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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.


  • Babak Mohammadi
  • Nguyen Thi Thuy Linh
  • Quoc Bao Pham
  • Ali Najah Ahmed
  • Jana Vojteková
  • Yiqing Guan
  • S. I. Abba
  • Ahmed El-Shafie
Externa organisationer
  • Hohai University
  • National Cheng Kung University
  • Thuyloi University
  • Duy Tan University
  • University Tenaga National
  • Constantine the Philosopher University in Nitra
  • Yusuf Maitama Sule University
  • University of Malaya
  • United Arab Emirates University

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Oceanografi, hydrologi, vattenresurser


Sidor (från-till)1738-1751
Antal sidor14
TidskriftHydrological Sciences Journal
Utgåva nummer10
StatusPublished - 2020 jul 26
Peer review utfördJa
Externt publiceradJa