Publication:
Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

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dc.citedby18
dc.contributor.authorEhteram M.en_US
dc.contributor.authorPanahi F.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMosavi A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid55368172500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57191408081en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:38:32Z
dc.date.available2023-05-29T09:38:32Z
dc.date.issued2022
dc.description.abstractPredicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112�mm/day, while it was 0.189�mm/day, 0.267�mm/day, 0.267�mm/day, 0.389�mm/day, 0.456�mm/day, and 0.512�mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation. Copyright � 2022 Ehteram, Panahi, Ahmed, Mosavi and El-Shafie.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo789995
dc.identifier.doi10.3389/fenvs.2021.789995
dc.identifier.scopus2-s2.0-85123444502
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123444502&doi=10.3389%2ffenvs.2021.789995&partnerID=40&md5=51d6cfc42129d572bcb0d47ce913e7aa
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26999
dc.identifier.volume9
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleFrontiers in Environmental Science
dc.titleInclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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