Early Detection of Harmful Algal Blooms Using Majority Voting Classifier: A Case Study of Alexandrium Minutum, Pseudo-Nitzschia Australis and Pseudo-Nitzschia Fraudulenta

Abir Loussaief, Abir Loussaief, Raïda Ktari, Yessine Hadj Kacem, Fatma Abdmouleh

2025

Abstract

Harmful algal blooms (HABs) severely damage the environment with significant adverse effects on marine life and human beings. An accurate prediction of HAB events is equally important in bloom management. This work investigates machine learning models to predict HAB occurrences, specifically focusing on three toxic species: Alexandrium minutum, Pseudonitzschia australis, and Pseudonitzschia fraudulenta. A majority voting ensemble method was implemented to improve the prediction performance by integrating the strength of different individual classifiers. Furthermore, the Synthetic Minority Oversampling Technique (SMOTE) was used to handle the class imbalance problem, which aided in enhancing bloom detection of rare occurrences. Compared with individual classifiers, the majority voting ensemble achieved better performance degrees with balanced accuracies of 99.09%, 99.57%, and 97.56% for Alexandrium minutum, Pseudonitzschia australis, and Pseudonitzschia fraudulenta datasets, respectively. These findings highlight the potential of combining ensemble methods and data augmentation for improving HAB predictions, thereby contributing to more active observing and mitigation strategies.

Paper Citation

in Harvard Style

Loussaief A., Ktari R., Hadj Kacem Y. and Abdmouleh F. (2025). Early Detection of Harmful Algal Blooms Using Majority Voting Classifier: A Case Study of Alexandrium Minutum, Pseudo-Nitzschia Australis and Pseudo-Nitzschia Fraudulenta. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 225-232. DOI: 10.5220/0013115000003890

in Bibtex Style

@conference{icaart25,
author={Abir Loussaief and Raïda Ktari and Yessine Hadj Kacem and Fatma Abdmouleh},
title={Early Detection of Harmful Algal Blooms Using Majority Voting Classifier: A Case Study of Alexandrium Minutum, Pseudo-Nitzschia Australis and Pseudo-Nitzschia Fraudulenta},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013115000003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Early Detection of Harmful Algal Blooms Using Majority Voting Classifier: A Case Study of Alexandrium Minutum, Pseudo-Nitzschia Australis and Pseudo-Nitzschia Fraudulenta
SN - 978-989-758-737-5
AU - Loussaief A.
AU - Ktari R.
AU - Hadj Kacem Y.
AU - Abdmouleh F.
PY - 2025
SP - 225
EP - 232
DO - 10.5220/0013115000003890
PB - SciTePress