Utilizing machine learning techniques for enhanced water quality monitoring

Water quality is an important issue for environmental health. It directly impacts human well-being, ecosystem sustainability and socioeconomic development. This paper provides an overview for water quality assesment by integrating traditional methods with computational technology. Dimensionality reduction is considered an essential preprocessing step in any data analysis task which can be performed by using either feature selection or feature extraction methods. In this study, we propose an autoencoder-based feature selection method that can be used with both labeled and unlabeled data. It can be implemented with an arbitrary number of hidden layers in the symmetric encoder part of the autoencoder and provides results that compare favorably with the results provided by computationally more expensive methods and also provides a quantitatively ordered rank of features for the features in the dataset. Also, our proposed method for water quality assessment has demonstrated remarkable success in efficiently managing and interpreting complex datasets, offering a promising pathway toward effective environmental stewardship and sustainable water resource management. Through its implementation, we aim to contribute to the preservation and protection of water quality for the benefit of present and future generations.