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Dr. Md Azizul Bari works as a Subject Matter Expert under the Climate Change Impact and Adaptation Task Force (CCIA) at the Academy of Sciences Malaysia. He has obtained his Ph.D. in Environment and Development from The National University of Malaysia, MBA and BBA in Finance and Banking from the University of Rajshahi, Bangladesh. He's interested in climate change adaptation and mitigation, sustainable development, low carbon, disaster-resilient cities and climate vulnerability.
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.
Labonnah Farzana Rahman; Mohammad Marufuzzaman; Lubna Alam; Azizul Bari; Ussif Rashid Sumaila; Lariyah Mohd Sidek. Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production. Sustainability 2021, 13, 9124 .
AMA StyleLabonnah Farzana Rahman, Mohammad Marufuzzaman, Lubna Alam, Azizul Bari, Ussif Rashid Sumaila, Lariyah Mohd Sidek. Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production. Sustainability. 2021; 13 (16):9124.
Chicago/Turabian StyleLabonnah Farzana Rahman; Mohammad Marufuzzaman; Lubna Alam; Azizul Bari; Ussif Rashid Sumaila; Lariyah Mohd Sidek. 2021. "Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production." Sustainability 13, no. 16: 9124.