Discoverable Hidden Patterns in Water Quality Through AI, LLMs, and Transparent Remote Sensing

Clean water is a basic human need and necessary for environmental sustainability, particularly in rural areas with limited supplies of water where basic methods for monitoring water quality are seriously impeded. Combining explainable artificial intelligence (XAI) and large language models (LLMs) with remote sensing offers a possible solution. In this review, we examine an innovative framework that leverages satellite and UA V data to assess water quality through pioneering AI techniques. The proposed framework strongly emphasizes using LLMs for better data processing and Perspective formation, with XAI techniques to ensure trust at both stakeholder level and model visibility. Application of this approach could significantly improve real-time monitoring and notably valuable outcomes to conserve water supplies in resource-constrained environments. The framework is designed to be scalable and flexible to fit the specific environmental context of either rural environments or socioeconomic variables to ensure any water quality-monitoring program remains sustainable over time. This analysis shows how we can combine such emergent technology as AI with remote sensing to tackle large-scale water quality problems and open up new ways ofmonitoring our precious aquatic resources.