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

Sustainability

卷:13

Md Azizul Bari¹  Ussif Rashid Sumaila²  Lubna Alam²  Labonnah Farzana Rahman²  Mohammad Marufuzzaman³  Lariyah Mohd Sidek³ 

[1] Academy of Sciences Malaysia, Kuala Lumpur 50480, Malaysia;

[2] Institute for Environment and Development, Universiti Kebangsaan Malaysia, Putrajaya 43600, Malaysia;

[3] Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia;

关键词: climate change;    machine learning;    marine fish;    marine aquaculture;    feature importance;   

DOI  :  10.3390/su13169124

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown