Weight Prediction for Fishes in Setiu Wetland, Terengganu, using Machine Learning Regression Model

¹ Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
² Mangrove Research Unit, Institute of Oceanography & Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
³ Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
⁴ Institute of Tropical Aquaculture Tropical and Fisheries, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

^* Corresponding author: sititafzil@umt.edu.my

Abstract

Predicting fish weight holds several essential implications in ecology, such as population assessment, trophic interactions within ecosystems, biodiversity studies of fish communities, ecosystem modelling, habitat evaluation for different fish species, climate change research, and support fisheries management practices. The objective of the studies is to analyse the prediction performance of machine learning (ML) regression models by applying different statistical analysis techniques. This study collected biometric measurements (total length and body weight) for 19 fish families from three locations in Setiu Wetland, Terengganu, captured between 2011 and 2012. The study adopts two regression types: Linear Regression (i.e., Multiple Linear, Lasso, and Ridge model) and Tree-based Regression (i.e., Decision Tree, Random Forest, and XGBoost model). Mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R²) were used to evaluate performance. The results showed that the proposed ML regression models successfully predicted fish weight in Setiu Wetlands, and the Tree-based Regression model provides more accurate prediction results than the Linear Regression model. As a result, Random Forest is the best predictive model out of the six suggested ML regressions, with the highest accuracy at 96.1% and the lowest RMSE and MAE scores at 3.352 and 0.880, respectively. In conclusion, the use of machine learning is crucial for rapid, precise, and cost-effective fish weight measurement. By incorporating weight prediction into ecological research and management practices, we may make informed decisions supporting the conservation and sustainable use of fish populations and their habitats.