Mohammadi, Babak and Linh, Nguyen Thi Thuy and Pham, Quoc Bao and Ahmed, Ali Najah and Vojtekova, Jana and Guan, Yiqing and Abba, S. and El-Shafie, Ahmed (2020) Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrological Sciences Journal-Journal des Sciences Hydrologiques, 65 (10). pp. 1738-1751. ISSN 0262-6667, DOI https://doi.org/10.1080/02626667.2020.1758703.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Streamflow; Estimation; Time series models; Adaptive neuro-fuzzy inference system (ANFIS); Shuffled frog leaping algorithm (SFLA) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 08 Mar 2023 02:46 |
| Last Modified: | 08 Mar 2023 02:46 |
| URI: | http://eprints.um.edu.my/id/eprint/37621 |
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