A Review on Deep Learning Approaches to Address Multi-Class Imbalance: An Emphasis on Water Quality Data

Abstract

The deep learning methods for dealing with multi-class imbalance in water quality data are comprehensively examined in this paper. The findings show a notable increase in publications, especially since 2021, and a clear preference for deep learning techniques when classifying imbalanced data.

Background: A thorough review of the body of literature included articles from significant digital libraries that were published between January 2012 and December 2024. Based on several important factors, such as commonly used datasets and types, years of publication, different sources, research and empirical types, assessment measures, and development tools, 59 articles were chosen and examined.

Objective: This study examines how deep learning techniques respond to data that is unbalanced when there are multiple classes and how the deep learning models' large capacity and intricate structures make them appropriate for these kinds of tasks, with an emphasis on water datasets.

Conclusion: The paper emphasizes the difficulties with data diversity and computing efficiency, along with possible solutions for reliable real-world applications. It also discusses innovative solutions that enhance the reliability of real-world applications.