Aqi Index Analyzer Using Machine Learning

• Author(s):

  B Aditya | Mr. Ritesh Kumar

• Keywords:

  • MachineLearning • Deep Learning • Random Forest Regression • Long Short-Term Memory (LSTM) • Convolutional Neural Network (CNN) • Environmental Factors • Weather Data • Feature Selection • Time-Series Forecasting • Precision Agriculture • Data

• Abstract:

  Air Quality Index AQI Prediction Is A Critical Public Health Task That Involves Leveraging Data-driven Methods To Forecast Pollutant Concentrations And The Resulting AQI Under Dynamic Environmental And Meteorological Conditions1. This Research Explores The Application Of Advanced Statistical And Machine Learning ML Techniques, Including Ensemble Models And Deep Neural Networks, To Forecast AQI Based On Factors Such As Primary Pollutants PM2.5, NO2, O, Weather Patterns (temperature, Wind Speed), And Temporal Attributes. Comprehensive Datasets Collected From Multiple Monitoring Stations And Meteorological Sources Are Utilized To Train And Evaluate Predictive Algorithms, With Rigorous Feature Selection Strategies Enhancing Model Performance. Experimental Results Indicate That Ensemble Methods Like Random Forest (RF) And Deep Learning (DL) Models Such As Long Short-Term Memory (LSTM) Significantly Outperform Traditional Approaches, Achieving High Accuracy In AQI Estimation. The Proposed ML Models Are Designed To Support Public Health Agencies, Environmental Regulators, And Citizens In Making Data-informed Decisions That Mitigate Exposure Risks And Improve Public Safety. Future Work Will Focus On Integrating Real-time Sensor Data And Refining Quantized ML Frameworks For Efficient Deployment On Edge Devices.

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Other Details

• Paper id:

  IJSARTV11I11104323

• Published in:

  Volume: 11 Issue: 11 November 2025

• Publication Date:

  2025-11-20

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