Abstract :

Urban Water Quality Monitoring Is Essential For Protecting Public Health, Supporting Sustainable Environmental Management, And Ensuring The Efficient Operation Of Water Distribution Systems. Conventional Monitoring Methods Rely On Periodic Sampling And Laboratory Analysis, Which Often Fail To Provide Timely Detection Of Water Contamination Events. The Widespread Deployment Of Internet Of Things (IoT) Sensors, Remote Sensing Platforms, Weather Stations, And Smart City Infrastructures Has Enabled Continuous Collection Of Ubiquitous Data For Intelligent Water Quality Assessment. This Paper Presents A Data-driven Framework For Predicting Urban Water Quality By Integrating Ubiquitous Sensing Data With Advanced Machine Learning Techniques. The Proposed Approach Utilizes Heterogeneous Environmental, Meteorological, And Water Distribution Data To Accurately Forecast Critical Water Quality Parameters Such As PH, Turbidity, Dissolved Oxygen, And Conductivity. The Framework Enhances Prediction Accuracy, Supports Real-time Monitoring, And Enables Early Detection Of Pollution Events While Reducing Operational Costs. The Proposed System Contributes To Sustainable Urban Water Resource Management And Smart City Development.

Published:

22-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1120-1125

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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