Analysing the impact on groundwater quality using dynamic programming and vision transformer

Abstract

The demand for sustainable water sources has intensified the focus on ground water harvesting, necessitating rigorous quality analysis for diverse applications. The existing methods for ground water quality analysis are briefly discussed, revealing a research gap in optimizing the sampling and testing process. To address this gap, we employ Dynamic Programming, which is utilized to streamline the sampling and testing process by selecting optimal locations and times. This study proposes a new methodology that combines Dynamic Programming (DP) and the Vision Transformer (ViT) model to evaluate the impact on ground water quality efficiently. This optimization not only enhances data comprehensiveness but also minimizes resource utilization, a crucial aspect in resource-constrained environments. The incorporation of the Vision Transformer model into the methodology brings a novel dimension to ground water quality analysis. ViT, a cutting-edge deep learning model for image analysis, is adapted to process visual data from ground water samples. This includes identifying contaminants, suspended solids, and microbial content, providing a more holistic understanding of ground water quality than traditional methods. Results from our integrated approach demonstrate its efficiency and accuracy in assessing ground water quality. It achieves a 10–15% boost in sampling optimization efficiency, 5–10% improvement in visual data accuracy, and 5–10% increase in precision. DP-ViT reduces resource utilization by 15–20% and consistently offers 5–10% higher data coverage.