@Article{cmc.2023.038045,
AUTHOR = {Wen Yee Wong, Khairunnisa Hasikin, Anis Salwa Mohd Khairuddin, Sarah Abdul Razak, Hanee Farzana Hizaddin, Mohd Istajib Mokhtar, Muhammad Mokhzaini Azizan},
TITLE = {A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {2},
PAGES = {1361--1384},
URL = {http://www.techscience.com/cmc/v76n2/54031},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.038045}
}