A Survey On Data-Driven Air Quality Prediction Using Machine Learning

G GAYATHRI; M SAIVARDHAN; P NAVEEN; V LEELAVATHI; N GURU CHARAN; B VENKATESWARLU

A Survey On Data-Driven Air Quality Prediction Using Machine Learning

Authors: G GAYATHRI, M SAIVARDHAN, P NAVEEN, V LEELAVATHI, N GURU CHARAN, B VENKATESWARLU

Unique Paper ID: 177968
Volume: 11
Issue: 12
PageNo: 1769-1773

Keywords: Air Quality Index Linear Regression Artificial Neural Network Decision tree regression Air pollution Detection Machine learning Air quality statistical analysis Air pollutant feature extraction Real-time air quality prediction

Abstract:
The project aims to ensure optimal air quality in targeted urban areas by employing a sophisticated air quality monitoring system that collects data on contaminants from various locations. Pollutants like particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and others, accumulate in the atmosphere, causing a deterioration in air quality and posing serious risks to both human health and the environment. Severe pollution detection and traffic routing may benefit from the prediction of the air pollution index. Using sophisticated algorithms to represent the complex connections between these factors is a promising area in machine learning. Comparing different machine learning techniques, including SARIMA, SVM, and LSTM, in order to forecast the Ahmedabad air quality index for Gujarat, India, is the aim of this piece of work. The project leverages advanced machine learning algorithms to analyze historical data on air quality and predict air quality index. By accurately predicting air quality levels, the project can help individuals and authorities take preventive measures to reduce exposure to pollutants and improve public health. These models measure local air contamination and collect data on pollutant concentrations. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 1769-1773

A Survey On Data-Driven Air Quality Prediction Using Machine Learning

Available:https://ijirt.org/Article?manuscript=177968

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