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Article

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

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Institute for Environment and Development, Universiti Kebangsaan Malaysia, Putrajaya 43600, Malaysia
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Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
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Academy of Sciences Malaysia, Kuala Lumpur 50480, Malaysia
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Institute for the Oceans and Fisheries, Faculty of Science, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Authors to whom correspondence should be addressed.
Academic Editor: Gioele Capillo
Sustainability 2021, 13(16), 9124; https://0-doi-org.brum.beds.ac.uk/10.3390/su13169124
Received: 28 June 2021 / Revised: 21 July 2021 / Accepted: 23 July 2021 / Published: 14 August 2021
The 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 ML-based 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. View Full-Text
Keywords: climate change; machine learning; marine fish; marine aquaculture; feature importance climate change; machine learning; marine fish; marine aquaculture; feature importance
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MDPI and ACS Style

Rahman, L.F.; Marufuzzaman, M.; Alam, L.; Bari, M.A.; Sumaila, U.R.; Sidek, L.M. Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production. Sustainability 2021, 13, 9124. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169124

AMA Style

Rahman LF, Marufuzzaman M, Alam L, Bari MA, Sumaila UR, Sidek LM. Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production. Sustainability. 2021; 13(16):9124. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169124

Chicago/Turabian Style

Rahman, Labonnah F., Mohammad Marufuzzaman, Lubna Alam, Md A. Bari, Ussif R. Sumaila, and Lariyah M. Sidek 2021. "Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production" Sustainability 13, no. 16: 9124. https://0-doi-org.brum.beds.ac.uk/10.3390/su13169124

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