Abstract :

Urban Water Quality Monitoring Is Essential For Ensuring Public Health, Environmental Sustainability, And Efficient Resource Management. Traditional Water Quality Assessment Methods Rely Heavily On Periodic Manual Sampling And Laboratory Analysis, Which Are Often Time-consuming, Costly, And Unable To Provide Real-time Insights. With The Rapid Growth Of Ubiquitous Data Sources Such As Internet Of Things (IoT) Sensors, Satellite Observations, Environmental Monitoring Systems, And Crowd-sourced Information, New Opportunities Have Emerged To Develop Intelligent Prediction Models For Urban Water Quality Management. This Study Proposes A Data-driven Framework For Predicting Urban Water Quality Using Ubiquitous Data Collected From Multiple Heterogeneous Sources. Machine Learning Algorithms Are Employed To Analyze Large Volumes Of Spatial And Temporal Data, Enabling Accurate Prediction Of Key Water Quality Parameters Such As PH, Turbidity, Dissolved Oxygen, And Contaminant Levels. The Proposed Model Integrates Real-time Sensor Data With Historical Environmental Datasets To Identify Patterns And Trends That Influence Water Quality In Urban Ecosystems. Experimental Evaluation Demonstrates That The System Improves Prediction Accuracy And Supports Early Detection Of Potential Water Contamination Events. The Proposed Approach Can Assist City Authorities And Environmental Agencies In Making Timely And Informed Decisions For Sustainable Water Resource Management And Smart City Development.

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

14 - 23

Section:

Articles

License:

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

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