plSSN : 0374-8111 | elSSN : 2287-8815
plSSN : 0374-8111 | elSSN : 2287-8815
plSSN : 0374-8111elSSN : 2287-8815
Antarctic Silverfish Pleuragramma antarctica Nautical Area ScatteringCoefficient (NASC) Prediction Using a Machine Learning-Based Model
이사라·오우석·나형술1,2·손우주1·김정훈3·이경훈4*
국립부경대학교 어업기술안전연구소, 1한국해양과학기술원 극지연구소 해양대기연구본부, 2한국과학기술연합대학교대학원 극지과학과, 3한국해양
과학기술원 극지연구소 생명과학본부, 4국립부경대학교 해양생산관리학부
This study aimed to develop a machine learning-based prediction model for the nautical area scattering coefficient (NASC) of the Antarctic silverfish Pleuragramma antarcticum, a key species in the Southern Ocean. Acoustic survey data from the Ross Sea from 2018 to 2023 were integrated with environmental variables, including depth, temperature, salinity, survey period, survey area, and grid location, to construct Random Forest regression models. Separate models were trained on the adults and juveniles. For adults, continuous variables were standardized using z-scores. Meanwhile, juvenile models were standardized using raw values. Model training was performed using MATLAB TreeBagger with grid search optimization. The performance was evaluated by hold-out validation. The adult model achieved high accuracy (R²?0.76, RMSE?2.10), with depth, temperature, and salinity identified as the most influential predictors. The juvenile model showed lower explanatory power (R²?0.38, RMSE?2.54), often underestimating high NASC values. Adults are more strongly governed by physical conditions, whereas juveniles are influenced by additional biological or ecological factors. Random Forest models can effectively predict adult silverfish NASC using limited environmental inputs, supporting the improved interpretation of acoustic data and ecosystem-based management in polar environments.
Machine learning, Randomforest, Antarctic silverfish, Nautical area acoustic coefficient