Abstract :

Air Pollution Has Become A Critical Environmental And Public Health Concern In Many Urban And Industrial Regions Across The World. Accurate Prediction Of Air Pollution Levels Can Help Authorities And Citizens Take Preventive Actions To Reduce Health Risks And Environmental Damage. This Study Focuses On The Development Of A Predictive Model For Forecasting Air Pollution Levels Using Data-driven Techniques. The Proposed System Utilizes Historical Air Quality Data, Meteorological Parameters Such As Temperature, Humidity, Wind Speed, And Atmospheric Pressure, Along With Pollutant Concentration Levels Including PM2.5, PM10, CO, NO₂, And SO₂. Machine Learning Algorithms Such As Linear Regression, Random Forest, And Support Vector Machines Are Employed To Analyze Patterns And Relationships Between Environmental Variables And Pollution Levels. The System Processes Large Datasets, Performs Data Preprocessing, Feature Selection, And Model Training To Generate Accurate Predictions Of Future Air Quality Conditions. The Predicted Results Can Assist Environmental Agencies, Urban Planners, And The Public In Making Informed Decisions Regarding Pollution Control And Health Safety Measures. Experimental Results Indicate That Machine Learning Models Significantly Improve Prediction Accuracy Compared To Traditional Statistical Approaches. The Proposed Framework Provides An Efficient And Scalable Solution For Real-time Air Pollution Monitoring And Forecasting, Contributing To Smarter Environmental Management And Sustainable Urban Development.

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

33 - 40

Section:

Articles

License:

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

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