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Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm

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dc.citedby4
dc.contributor.authorEhteram M.en_US
dc.contributor.authorPanahi F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid55368172500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:38:21Z
dc.date.available2023-05-29T09:38:21Z
dc.date.issued2022
dc.descriptionalgorithm; artificial neural network; evaporation; optimization; uncertainty analysis; Kuala Terengganu; Kuantan; Malaysia; Pahang; Terengganu; West Malaysia; water; algorithm; Malaysia; uncertainty; Algorithms; Malaysia; Neural Networks, Computer; Uncertainty; Wateren_US
dc.description.abstractEvaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-021-16301-3
dc.identifier.epage10701
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85123442098
dc.identifier.spage10675
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123442098&doi=10.1007%2fs11356-021-16301-3&partnerID=40&md5=9b2eb136613a13cb031887466267087e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26981
dc.identifier.volume29
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titlePredicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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