Deep hierarchical cluster analysis for assessing the water quality indicators for sustainable groundwater

Abstract

Groundwater quality assessment is a paramount aspect of ensuring the availability of safe drinking water. The conventional reliance on individual parameter thresholds often overlooks intricate interdependencies within the dataset. To overcome this, deep learning algorithms are employed to automatically extract meaningful features from multidimensional data. This innovative approach aims to capture complex relationships between water quality parameters that may be missed by traditional methods. The research identifies a significant gap in current groundwater quality assessment methodologies, emphasizing the need for a more data-driven approach. This research addresses the limitations of traditional methods by introducing a pioneering approach that integrates deep learning and hierarchical cluster analysis to identify comprehensive water quality indicators. The study collects data from diverse monitoring wells, encompassing chemical, physical, and biological parameters. By applying deep cluster analysis to the feature-extracted data, latent patterns and relationships among water quality parameters are unveiled. This clustering method reveals hidden structures within the dataset, leading to the identification of water quality indicators that consider both individual parameters and their interactions. The proposed method in this research offers an understanding of groundwater quality dynamics, contributing to the advancement of water resource management strategies. The results of the deep cluster analysis provide a more comprehensive and accurate representation of groundwater quality, enabling better-informed decision-making for sustainable water resource utilization.