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

Labonnah Farzana Rahman, Mohammad Marufuzzaman, Lubna Alam, Md Azizul Bari, Ussif Sumaila and Lariyah Mohd Sidek
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Labonnah Farzana Rahman: Institute for Environment and Development, Universiti Kebangsaan Malaysia, Putrajaya 43600, Malaysia
Mohammad Marufuzzaman: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
Lubna Alam: Institute for Environment and Development, Universiti Kebangsaan Malaysia, Putrajaya 43600, Malaysia
Md Azizul Bari: Academy of Sciences Malaysia, Kuala Lumpur 50480, Malaysia
Lariyah Mohd Sidek: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia

Sustainability, 2021, vol. 13, issue 16, 1-14

Abstract: 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 R 2 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.

Keywords: climate change; machine learning; marine fish; marine aquaculture; feature importance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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