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Developing an ensembled machine learning prediction model for marine fish and aquaculture production

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dc.citedby3
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.accessioned2023-05-29T09:06:17Z
dc.date.available2023-05-29T09:06:17Z
dc.date.issued2021
dc.description.abstractThe fishing industry is identified as a strategic sector to raise domestic protein production and supply in Malaysia. Global changes in climatic variables have impacted and continue to impact marine fish and aquaculture production, where machine learning (ML) methods are yet to be extensively used to study aquatic systems in Malaysia. ML-based algorithms could be paired with feature importance, i.e., (features that have the most predictive power) to achieve better prediction accuracy and can provide new insights on fish production. This research aims to develop an MLbased prediction of marine fish and aquaculture production. Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000�2019) of climatic variables and fish production data were used to train and test the ML models. Finally, an ensemble approach named voting regression combines those three ML models. Performance matrices are generated and the results showed that the ensembled ML model obtains R2 values of 0.75, 0.81, and 0.55 for marine water, freshwater, and brackish water, respectively, which outperforms the single ML model in predicting all three types of fish production (in tons) in Malaysia. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9124
dc.identifier.doi10.3390/su13169124
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85113807873
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85113807873&doi=10.3390%2fsu13169124&partnerID=40&md5=0ab683d17c71fab595ffe7ac46d714de
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26043
dc.identifier.volume13
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleDeveloping an ensembled machine learning prediction model for marine fish and aquaculture productionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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