Citation

Naim, S M and Das, Prosenjit and Tiang, Jun Jiat and Nahid, Abdullah-Al (2025) Aquaculture Water Quality Classification Using XGBoost Classifier Model Optimized by the Honey Badger Algorithm with SHAP and DiCE-Based Explanations. Water, 17 (20). p. 2993. ISSN 2073-4441

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Abstract

Water quality is an essential part of maintaining a healthy environment for fish farming. The quality of the water is related to a few of the chemical and biological characteristics of water. The conventional evaluation methods of the water quality are often time-consuming and may overlook complex interdependencies among multiple indicators. This study has proposed a robust machine learning framework for aquaculture water quality classification by integrating the Honey Badger Algorithm (HBA) with the XGBoost classifier. The framework enhances classification accuracy and incorporates explainability through SHAP and DiCE, thereby providing both predictive performance and transparency for practical water quality management. For reliability, the dataset has been randomly shuffled, and a custom 5-fold cross-validation strategy has been applied. Later, through the metaheuristic-based HBA, feature selections and hyperparameter tuning have been performed to improve and increase the prediction accuracy. The highest accuracy of 98.45% has been achieved by a particular fold, whereas the average accuracy is 98.05% across all folds, indicating the model’s stability. SHAP analysis reveals Ammonia, Nitrite, DO, Turbidity, BOD, Temperature, pH, and CO2 as the topmost water quality indicators. Finally, the DiCE analysis has analyzed that Temperature, Turbidity, DO, BOD, CO2, pH, Ammonia, and Nitrite are more influential parameters of water quality.

Item Type:	Article
Uncontrolled Keywords:	DiCE, fish, honey badger algorithm, machine learning, SHAP, water, XGBoost
Subjects:	S Agriculture > SH Aquaculture. Fisheries. Angling
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	09 Dec 2025 03:42
Last Modified:	09 Dec 2025 03:42
URII:	http://shdl.mmu.edu.my/id/eprint/14979

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