Abstract            Volume:12  Issue-1  Year-2024         Original Research Articles

Online ISSN : 2347 - 3215 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcret@gmail.com

Water quality encompasses various factors that determine water's suitability for different purposes and its impact on the environment and human health. It includes chemical, physical, biological, and radiological characteristics. Studying water quality is vital for ensuring safe drinking water, supporting ecosystems, sustaining agriculture and industries, facilitating recreational activities, and adhering to regulatory standards. Techniques for assessing water quality involve measuring physical parameters like temperature and turbidity, chemical analysis for pH, dissolved oxygen, nutrients, and heavy metals, biological assessments using indicators like macroinvertebrates, microbiological testing for bacterial contamination, remote sensing, flow measurement, and data analysis methods. The Water Quality Index (WQI) simplifies complex water quality data into a single value or category, offering an accessible assessment of overall water quality conditions. It combines various parameters, assigns weights based on their importance, and aggregates them into an index value or qualitative classification, aiding in effective understanding and communication of water quality information. Artificial intelligence (AI) significantly contributes to water quality analysis by leveraging machine learning, data analytics, and neural networks. AI processes vast amounts of water quality data, predicts changes, identifies patterns, optimizes monitoring systems, supports decision-making, improves water treatment processes, and enhances early warning systems. Using AI enables analysts to gain more efficient, accurate, and timely insights into water quality conditions, enabling better-informed management strategies and the conservation of water resources for human and environmental well-being.

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