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Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

accuracy assessment; algorithm; artificial neural network; design method; optimization; prediction; suspended sediment; Iran; algorithm; animal; Cetacea; Iran; Algorithms; Animals; Iran; Neural Networks, Computer; Whales

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Main Authors: Ehteram M., Ahmed A.N., Latif S.D., Huang Y.F., Alizamir M., Kisi O., Mert C., El-Shafie A.
Other Authors: 57113510800
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-266162023-05-29T17:12:50Z Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction Ehteram M. Ahmed A.N. Latif S.D. Huang Y.F. Alizamir M. Kisi O. Mert C. El-Shafie A. 57113510800 57214837520 57216081524 55807263900 57188682009 6507051085 54898539300 16068189400 accuracy assessment; algorithm; artificial neural network; design method; optimization; prediction; suspended sediment; Iran; algorithm; animal; Cetacea; Iran; Algorithms; Animals; Iran; Neural Networks, Computer; Whales There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers�whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)�for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5�20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:12:50Z 2023-05-29T09:12:50Z 2021 Article 10.1007/s11356-020-10421-y 2-s2.0-85089862705 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089862705&doi=10.1007%2fs11356-020-10421-y&partnerID=40&md5=5ccaa5f2ae6b4982f9a7d8a5332f9131 https://irepository.uniten.edu.my/handle/123456789/26616 28 2 1596 1611 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description accuracy assessment; algorithm; artificial neural network; design method; optimization; prediction; suspended sediment; Iran; algorithm; animal; Cetacea; Iran; Algorithms; Animals; Iran; Neural Networks, Computer; Whales
author2 57113510800
author_facet 57113510800
Ehteram M.
Ahmed A.N.
Latif S.D.
Huang Y.F.
Alizamir M.
Kisi O.
Mert C.
El-Shafie A.
format Article
author Ehteram M.
Ahmed A.N.
Latif S.D.
Huang Y.F.
Alizamir M.
Kisi O.
Mert C.
El-Shafie A.
spellingShingle Ehteram M.
Ahmed A.N.
Latif S.D.
Huang Y.F.
Alizamir M.
Kisi O.
Mert C.
El-Shafie A.
Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
author_sort Ehteram M.
title Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
title_short Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
title_full Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
title_fullStr Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
title_full_unstemmed Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction
title_sort design of a hybrid ann multi-objective whale algorithm for suspended sediment load prediction
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806423479640129536
score 13.222552

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