• Pudi Neelima
• Dr. S. Sridhar
• Amit Pote

The importance of monitoring water quality is often neglected while dealing with the rising population’s needs. In association with the project, an Adaptive Frequency Analysis (Adp-FA) algorithm is utilized to ascertain predicted values of water quality while also estimating the risks involved based on the sensor data. Adp-FA retrieves indicators such as physical, chemical, biological and environmental from large datasets through pre processing, normalization and clustering techniques. Adp-FA, when tested, had the most accurate results of 99% as well as the lowest RMSE error rate of 0.086% when compared to other models such as ANN and Random Forest. The dataset used in the project in conjunction with other factors uses an Indian state water supply dataset to ascertain the presence of ECOLIFORM and ECOLI bacteria. The proposed approach is focused on building ’scalability’ with effective measures for feature selection reliability while predicting risks. This work proves that Adp-FA can be applied in smart water supply systems and help in resource management betterment.

Adp-FA, Water, ANN

"TOWARDS SMART WATER SUPPLY: PREDICTING RISK WITH ADVANCED SENSOR DATA PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.b193-b199, January-2025, Available :http://www.jetir.org/papers/JETIR2501146.pdf

"TOWARDS SMART WATER SUPPLY: PREDICTING RISK WITH ADVANCED SENSOR DATA PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppb193-b199, January-2025, Available at : http://www.jetir.org/papers/JETIR2501146.pdf