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

Simple item page
dc.citedby35
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorAlizamir M.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorMert C.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57188682009en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid54898539300en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:12:50Z
dc.date.available2023-05-29T09:12:50Z
dc.date.issued2021
dc.descriptionaccuracy assessment; algorithm; artificial neural network; design method; optimization; prediction; suspended sediment; Iran; algorithm; animal; Cetacea; Iran; Algorithms; Animals; Iran; Neural Networks, Computer; Whalesen_US
dc.description.abstractThere 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-020-10421-y
dc.identifier.epage1611
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85089862705
dc.identifier.spage1596
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089862705&doi=10.1007%2fs11356-020-10421-y&partnerID=40&md5=5ccaa5f2ae6b4982f9a7d8a5332f9131
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26616
dc.identifier.volume28
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleDesign of a hybrid ANN multi-objective whale algorithm for suspended sediment load predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS