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

Aug 4, 2026

Predicting fish weight supports key ecological tasks, including population assessment, trophic interaction analysis, biodiversity evaluation of fish communities, ecosystem modelling, habitat suitability across species, climate-change research, and practical fisheries management. The study evaluates machine-learning regression performance using biometric data (total length and body weight) from 19 fish families collected at three sites in Setiu Wetland, Terengganu, during 2011–2012. Linear Regression and tree-based Regression (Decision Tree, Random Forest, XGBoost) are compared using MAE, RMSE, and R².

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Fish weight measurement and ecological importance

Machine learning and regression background

Linear regression and regularization methods

Tree-based regression models

Weight Prediction for Fishes in Setiu Wetland, Terengganu, using Machine Learning Regression Model Nurzuhrah Hassan1, Siti Tafzilmeriam Sheikh Abdul Kadir1,2*, Mohd Lokman Husain1 Behara Satyanarayana 1,2, Mohd Azmi Ambak, and Mazlan Abd.Ghaffar3,4 1Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia 2Mangrove Research Unit, Institute of Oceanography & Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia 3Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia 4Institute of Tropical Aquaculture Tropical and Fisheries, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia 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 costeffective 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. * Corresponding author: [sititafzil@umt.edu.my](sititafzil@umt.edu.my) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). 1 Introduction Predicting fish weight is crucial in ecology because it helps estimate fish population size and distribution, which is vital for monitoring and managing fish. This data is essential for assessing population changes and health [1, 2], understanding ecosystem trophic interactions, energy flow, and food web structure [3], and biodiversity studies to assess species diversity and abundance [4] . Additionally, fish weight data is vital for ecosystem modelling [3] and addressing factors like fishing and climate change's impact on fish populations and habitats, as shifts in fish weight can signal changes in growth rates, productivity, and species composition [5], ultimately contributing to the sustainable management of fisheries for setting catch limits, crafting effective fishing regulations, and implementing conservation strategies, thus ensuring the long-term viability of fish stocks [6] . Typically, the process of measuring the weight of fish is conducted manually on a perindividual basis. Nevertheless, this procedure is challenging, time-intensive, and stressful for the fish. Weight measurements on fresh or frozen fish can vary sign

FAQs

Why is predicting fish weight important in ecology?

Fish weight helps estimate population size and distribution, supports trophic interaction and food-web studies, and informs biodiversity assessments. It also contributes to ecosystem modelling and fisheries management.

What data and time span were used for the weight prediction study?

The study used biometric measurements (total length and body weight) for 19 fish families from three locations in Setiu Wetland, Terengganu, captured between 2011 and 2012.

Which regression models were compared and how were they evaluated?

Models included Linear Regression variants (Multiple Linear, Lasso, Ridge) and tree-based regressions (Decision Tree, Random Forest, XGBoost). Performance was assessed using MAE, RMSE, and coefficient of determination (R²).

What was the best performing model and its reported accuracy?

Random Forest performed best among the six models, achieving the highest accuracy of 96.1% and the lowest RMSE and MAE values (3.352 and 0.880, respectively).

Aug 4, 2026

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