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

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dc.citedby61
dc.contributor.authorMohammadi B.en_US
dc.contributor.authorLinh N.T.T.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorVojtekov� J.en_US
dc.contributor.authorGuan Y.en_US
dc.contributor.authorAbba S.I.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57195411533en_US
dc.contributor.authorid57211268069en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57188709053en_US
dc.contributor.authorid23477155800en_US
dc.contributor.authorid57208942739en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:08:47Z
dc.date.available2023-05-29T08:08:47Z
dc.date.issued2020
dc.descriptionCatchments; 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; Anuraen_US
dc.description.abstractAccurate 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.natureFinalen_US
dc.identifier.doi10.1080/02626667.2020.1758703
dc.identifier.epage1751
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85086121489
dc.identifier.spage1738
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086121489&doi=10.1080%2f02626667.2020.1758703&partnerID=40&md5=cf07bbec0762557a252eef99332bc89c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25383
dc.identifier.volume65
dc.publisherTaylor and Francis Ltd.en_US
dc.sourceScopus
dc.sourcetitleHydrological Sciences Journal
dc.titleAdaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time seriesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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