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A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

Wen Yee Wong1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6

1 Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
2 Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
3 Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
4 Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
5 Department of Science and Technology Studies, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
6 Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800, Nilai, Negeri Sembilan, Malaysia

* Corresponding Author: Khairunnisa Hasikin. Email: email

Computers, Materials & Continua 2023, 76(2), 1361-1384. https://doi.org/10.32604/cmc.2023.038045

Abstract

A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers included are AdaBoost, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, Extra Trees, Bagging, and the Multilayer Perceptron. This study also uses the Easy Ensemble Classifier, Balanced Bagging, and RUSBoost algorithm to evaluate multi-class imbalanced learning methods. The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity. This paper’s stacked ensemble deep learning (SE-DL) generalization model effectively classifies the water quality index (WQI) based on 23 input variables. The proposed algorithm achieved a remarkable average of 95.69%, 94.96%, 92.92%, and 93.88% for accuracy, precision, recall, and F1 score, respectively. In addition, the proposed model is compared against two state-of-the-art classifiers, the XGBoost (eXtreme Gradient Boosting) and Light Gradient Boosting Machine, where performance metrics of balanced accuracy and g-mean are included. The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean. However, the SE-DL model has a better and more balanced performance in the F1 score. The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class. The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard Synthetic Minority Oversampling Technique (SMOTE) approach to imbalanced datasets.

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Cite This Article

APA Style
Wong, W.Y., Hasikin, K., Khairuddin, A.S.M., Razak, S.A., Hizaddin, H.F. et al. (2023). A stacked ensemble deep learning approach for imbalanced multi-class water quality index prediction. Computers, Materials & Continua, 76(2), 1361-1384. https://doi.org/10.32604/cmc.2023.038045
Vancouver Style
Wong WY, Hasikin K, Khairuddin ASM, Razak SA, Hizaddin HF, Mokhtar MI, et al. A stacked ensemble deep learning approach for imbalanced multi-class water quality index prediction. Comput Mater Contin. 2023;76(2):1361-1384 https://doi.org/10.32604/cmc.2023.038045
IEEE Style
W.Y. Wong et al., "A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction," Comput. Mater. Contin., vol. 76, no. 2, pp. 1361-1384. 2023. https://doi.org/10.32604/cmc.2023.038045



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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