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Air Quality Prediction and Analysis using Embedded Sensors and Machine Learning

Yaddanapudi Guru Vardhan*, Shaik Nagul Meera **, Shaik Ajith***, Bogadula Hemanth****, Guruvulu Naidu Ponnada*****

*-***** Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India.

Periodicity:April - June'2025
DOI : https://doi.org/10.26634/jcom.13.1.21896

Abstract

This paper introduces a comprehensive, real-time air quality monitoring and alert system that combines sensor-based hardware with a machine learning model for data classification. The system integrates gas sensors MQ135, MQ2, MQ9, a DHT11 temperature and humidity sensor, and an Arduino Mega 2560 microcontroller. Sensor data is processed using a Python-based Random Forest classifier to predict pollution levels and environmental conditions. Alerts are triggered through buzzers, LCD displays, and GSM-based SMS notifications. Unlike traditional setups requiring Wi-Fi modules, the system transmits data to the ThingSpeak platform using serial communication and Python scripting. This approach offers a scalable and low-cost solution for detecting hazardous gases and abnormal temperatures, making it suitable for residential, industrial, and public safety applications.

Keywords

Arduino Mega, Gas Sensors, Machine Learning, Random Forest, IoT, ThingSpeak.

How to Cite this Article?

Vardhan, Y. G., Meera, S. N., Ajith, S., Hemanth, B., and Ponnada, G. N. (2025). Air Quality Prediction and Analysis using Embedded Sensors and Machine Learning. i-manager’s Journal on Computer Science, 13(1), 10-23. https://doi.org/10.26634/jcom.13.1.21896

References

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