Publication: Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
dc.citedby | 61 | |
dc.contributor.author | Mohammadi B. | en_US |
dc.contributor.author | Linh N.T.T. | en_US |
dc.contributor.author | Pham Q.B. | en_US |
dc.contributor.author | Ahmed A.N. | en_US |
dc.contributor.author | Vojtekov� J. | en_US |
dc.contributor.author | Guan Y. | en_US |
dc.contributor.author | Abba S.I. | en_US |
dc.contributor.author | El-Shafie A. | en_US |
dc.contributor.authorid | 57195411533 | en_US |
dc.contributor.authorid | 57211268069 | en_US |
dc.contributor.authorid | 57208495034 | en_US |
dc.contributor.authorid | 57214837520 | en_US |
dc.contributor.authorid | 57188709053 | en_US |
dc.contributor.authorid | 23477155800 | en_US |
dc.contributor.authorid | 57208942739 | en_US |
dc.contributor.authorid | 16068189400 | en_US |
dc.date.accessioned | 2023-05-29T08:08:47Z | |
dc.date.available | 2023-05-29T08:08:47Z | |
dc.date.issued | 2020 | |
dc.description | Catchments; Forecasting; Fuzzy neural networks; Fuzzy systems; Inference engines; Rivers; Stream flow; Water management; Adaptive neuro-fuzzy inference system; Forecasting accuracy; Forecasting modeling; Model inputs; Prediction accuracy; Runoff forecasting; Shuffled frog leaping algorithm (SFLA); Water resources systems; Fuzzy inference; accuracy assessment; algorithm; fuzzy mathematics; prediction; river flow; runoff; streamflow; time series; Anura | en_US |
dc.description.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. � 2020 IAHS. | en_US |
dc.description.nature | Final | en_US |
dc.identifier.doi | 10.1080/02626667.2020.1758703 | |
dc.identifier.epage | 1751 | |
dc.identifier.issue | 10 | |
dc.identifier.scopus | 2-s2.0-85086121489 | |
dc.identifier.spage | 1738 | |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086121489&doi=10.1080%2f02626667.2020.1758703&partnerID=40&md5=cf07bbec0762557a252eef99332bc89c | |
dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/25383 | |
dc.identifier.volume | 65 | |
dc.publisher | Taylor and Francis Ltd. | en_US |
dc.source | Scopus | |
dc.sourcetitle | Hydrological Sciences Journal | |
dc.title | Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication |