• Priyanka Kumari

Water quality remains a critical indicator of ecosystem health, public safety, and sustainable development. In recent decades, an increasing body of research has focused on both the assessment of water quality parameters and the development of innovative techniques for water quality improvement. This review synthesizes findings from 20 recent studies spanning a variety of methodologies and approaches. Traditional assessment methods including chemical titration, biological indicator surveys, and physical monitoring are discussed alongside emerging techniques that incorporate remote sensing, artificial intelligence (AI), and machine learning (ML) to provide real‐time, high‐resolution data. Several studies have demonstrated the value of optical remote sensing for mapping suspended solids and chlorophyll, while others highlight the application of recurrent neural networks for predicting nutrient concentrations. Further, integrated approaches combining sensor technology with AI data mining have shown promising improvements in accuracy, sensitivity, and speed in water quality monitoring. Advances in water quality index (WQI) modeling have refined our ability to aggregate complex datasets into a single, interpretable metric that guides management decisions. Despite these developments, challenges persist regarding sensor calibration, data heterogeneity, and the integration of multi-scale information. In addition, policy and infrastructure investments are needed to ensure that technological advancements translate into effective water resource management. This review critically examines current practices and proposes a framework that integrates conventional methods with novel, digitally enabled approaches. The synthesis provides key insights for researchers, water managers, and policymakers aiming to enhance water quality monitoring, reduce pollutant loads, and secure the long-term sustainability of freshwater resources.

Water Quality Monitoring, Remote Sensing, Artificial Intelligence in Water Management, Sensor Technologies, Water Quality Index, Machine Learning for Water Assessment

"Water Quality Assessment Parameters and Techniques to Improve Water Quality: A Literature Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.h249-h254, July-2025, Available :http://www.jetir.org/papers/JETIR2507732.pdf

"Water Quality Assessment Parameters and Techniques to Improve Water Quality: A Literature Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pph249-h254, July-2025, Available at : http://www.jetir.org/papers/JETIR2507732.pdf