Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm
algorithm; Iran; uncertainty; Algorithms; Iran; Neural Networks, Computer; Uncertainty
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2023
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my.uniten.dspace-252182023-05-29T16:07:24Z Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Ebrahimi M. Fai C.M. Huang Y.F. El-Shafie A. 57201068611 57113510800 57214837520 35249518400 57209555582 57214146115 55807263900 16068189400 algorithm; Iran; uncertainty; Algorithms; Iran; Neural Networks, Computer; Uncertainty Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T08:07:24Z 2023-05-29T08:07:24Z 2020 Article 10.1007/s11356-020-09876-w 2-s2.0-85087512372 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087512372&doi=10.1007%2fs11356-020-09876-w&partnerID=40&md5=6f07c348d169959f69bc8e4134f7bc5c https://irepository.uniten.edu.my/handle/123456789/25218 27 30 38094 38116 Springer Scopus |
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| description |
algorithm; Iran; uncertainty; Algorithms; Iran; Neural Networks, Computer; Uncertainty |
| author2 |
57201068611 |
| author_facet |
57201068611 Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Ebrahimi M. Fai C.M. Huang Y.F. El-Shafie A. |
| format |
Article |
| author |
Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Ebrahimi M. Fai C.M. Huang Y.F. El-Shafie A. |
| spellingShingle |
Banadkooki F.B. Ehteram M. Ahmed A.N. Teo F.Y. Ebrahimi M. Fai C.M. Huang Y.F. El-Shafie A. Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| author_sort |
Banadkooki F.B. |
| title |
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| title_short |
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| title_full |
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| title_fullStr |
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| title_full_unstemmed |
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| title_sort |
suspended sediment load prediction using artificial neural network and ant lion optimization algorithm |
| publisher |
Springer |
| publishDate |
2023 |
| _version_ |
1806425914008928256 |
| score |
13.214268 |