Publication:
Application of Machine Learning to Investigate the Impact of Climatic Variables on Marine Fish Landings

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dc.citedby1
dc.contributor.authorRahman L.F.en_US
dc.contributor.authorMarufuzzaman M.en_US
dc.contributor.authorAlam L.en_US
dc.contributor.authorBari M.A.en_US
dc.contributor.authorSumaila U.R.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorid36984229900en_US
dc.contributor.authorid57205234835en_US
dc.contributor.authorid37053462100en_US
dc.contributor.authorid55639915700en_US
dc.contributor.authorid6701840163en_US
dc.contributor.authorid35070506500en_US
dc.date.accessioned2023-05-29T09:37:22Z
dc.date.available2023-05-29T09:37:22Z
dc.date.issued2022
dc.description.abstractThe fisheries industry of Malaysia is known as the strategic sector that can help the country raise domestic food production and supply. This research proposed machine learning (ML) based prediction of marine fish landings to project fish supply and compare those projections with the observed data. Three ML models, i.e., linear regression (LR), decision tree (DT), and random forest (RF) regression, are applied to the dataset that contains 18�years of climatic variables and the marine fish landings (tonnes) information of 5 major states of Malaysia. The results suggest that the developed LR model shows an R2 value of 0.60 and 0.64 in the validation and testing phases. The DT and RF model indicates a significant improvement as the R2 values are 0.88 and 0.89 in the validation data and 0.89 and 0.86 in the testing data. Finally, we calculated the Nash�Sutcliffe efficiency (NSE) values, and the results indicated that RF based ML model has the highest NSE value of 0.86, which turns out to be the best fit for prediction. The developed ML models have utilized for the first time to predict the marine fish landing using environmental inputs collected from 5 different states of Malaysia. � 2022, The Author(s), under exclusive licence to The National Academy of Sciences, India.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s40009-022-01110-0
dc.identifier.epage248
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85126208296
dc.identifier.spage245
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85126208296&doi=10.1007%2fs40009-022-01110-0&partnerID=40&md5=6a00025f9939696b6d11b76f854a685f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26866
dc.identifier.volume45
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleNational Academy Science Letters
dc.titleApplication of Machine Learning to Investigate the Impact of Climatic Variables on Marine Fish Landingsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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