Abstract

Water quality is defined by its physical, chemical and biological parameters which are interrelated. In recent years, Artificial Neural Networks (ANN) have found some applications in the area of water quality modeling. Among the various familiar methods of water pollution detection, ANN based methods are one of the most effective methods which provides satisfactory outcome to the users than other methods. This paper elucidates several techniques for detecting water pollution, as applied in diverse regional water sources, and delineates their respective findings using appropriate formats such as tables and figures. These methods for water pollution detection, along with their corresponding data, figures, tables, and results, are comprehensively presented herein alongside the relevant citations and references. This study will help to understand diverse effective techniques for evaluating water quality and detecting water pollution level. Furthermore, this study will guide for refining and developing improved methodologies within this domain.

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