Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
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); W...
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2023
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my.uniten.dspace-253832023-05-29T16:08:47Z Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series Mohammadi B. Linh N.T.T. Pham Q.B. Ahmed A.N. Vojtekov� J. Guan Y. Abba S.I. El-Shafie A. 57195411533 57211268069 57208495034 57214837520 57188709053 23477155800 57208942739 16068189400 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 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. Final 2023-05-29T08:08:47Z 2023-05-29T08:08:47Z 2020 Article 10.1080/02626667.2020.1758703 2-s2.0-85086121489 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086121489&doi=10.1080%2f02626667.2020.1758703&partnerID=40&md5=cf07bbec0762557a252eef99332bc89c https://irepository.uniten.edu.my/handle/123456789/25383 65 10 1738 1751 Taylor and Francis Ltd. Scopus |
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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 |
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57195411533 Mohammadi B. Linh N.T.T. Pham Q.B. Ahmed A.N. Vojtekov� J. Guan Y. Abba S.I. El-Shafie A. |
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Mohammadi B. Linh N.T.T. Pham Q.B. Ahmed A.N. Vojtekov� J. Guan Y. Abba S.I. El-Shafie A. |
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Mohammadi B. Linh N.T.T. Pham Q.B. Ahmed A.N. Vojtekov� J. Guan Y. Abba S.I. El-Shafie A. Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| author_sort |
Mohammadi B. |
| title |
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| title_short |
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| title_full |
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| title_fullStr |
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| title_full_unstemmed |
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| title_sort |
adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series |
| publisher |
Taylor and Francis Ltd. |
| publishDate |
2023 |
| _version_ |
1806424176365404160 |
| score |
13.214268 |