International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
Prediction of Air Quality Index using Random Forest Algorithm Dipak Gaikar 1, Ujjwal Patel2, Om Vispute3,Sagar Singh4, Takshil Sanghvi5 1 Asst. Professor, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India 2 B.E. student, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India 3 B.E. student, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India 4 B.E. student, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India 5 B.E. student, Dept. of Computer Engineering, Rajiv Gandhi Institute of Technology, Maharashtra, India
--------------------------------------------------------------------------***----------------------------------------------------------------------the air quality status and associated health risks. AQI is a Abstract - Air pollution is a growing concern
numerical value ranging from 0 to 500, and it is calculated based on the levels of major air pollutants such as particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2).
worldwide, and it has serious implications on human health, the environment, and the economy. In this project, we explore the prediction of Air Quality Index (AQI) using the Random Forest algorithm. AQI is a measure of air pollution that is used to communicate the health risks associated with breathing polluted air. We use historical data collected from various air quality monitoring stations in a city and apply the Random Forest algorithm to predict AQI. This study aims to predict the AQI using machine learning algorithms. The AQI is a crucial indicator of air quality, and accurate forecasting can help mitigate the negative effects of air pollution on human health and the environment. The study utilizes data from air quality monitoring stations and meteorological sensors to train and evaluate various machine learning models, including Random Forest, Support Vector Regression, and Artificial Neural Networks. The accuracy of the algorithm is measured using the root mean square error . The mean square error and the mean absolute erro). The results indicate that the Random Forest algorithm performs well in predicting AQI and has the potential to be used as a tool to monitor air quality and help in making decisions to reduce air pollution. The findings of this study can be used by policy makers, city planners, and environmental agencies to design effective strategies to combat air pollution.
Various approaches have been developed to monitor and manage air quality, including regulatory policies, emission controls, and air quality forecasting. Air quality forecasting aims to predict future AQI levels using statistical and machine learning models based on historical data and meteorological factors. Machine learning techniques such as Linear Regression, support vector regression (SVR), and decision trees have been applied to air quality forecasting . Random Forest (RF) is a powerful machine learning algorithm that has been used for AQI prediction in recent studies.
2. OBJECTIVE
Keywords: Prediction, Machine Learning, Random Forest, Air Quality, P.M 2.5 , Root mean squared error( RMSE), Mean Squared error(MSE),mean absolute error (MAE).
1. INTRODUCTION Air pollution is a pervasive problem that affects millions of people worldwide, resulting in adverse health outcomes, environmental degradation, and economic losses. The World Health Organization (WHO) estimates that air pollution causes around 7 million premature deaths annually, making it one of the leading global health risks (WHO, 2021). Air Quality Index (AQI) is a measure of air pollution that provides information on
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Air quality forecasting that uses machine learning to predict the air quality index for a given region.
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To achieve better performance than the standard regression models.
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Our goal is for the model to accurately predict Air Quality Index for India as a whole.
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By forecasting Air Quality Index, we can track the main pollutants causing pollutants and the locations across India that are severely affected by pollutants.
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By creating a easily operated graphical user interface we will help the user to keep a track of the air quality index and its attribute on a single screen.
3. PROPOSED SYSTEM AQI is an important environmental indicator that is used to inform public health and policy decisions. The proposed System using an Enhanced approach using ANN (Artificial Neural Network) is tested using the dataset of list 5 years (2013-2018). The results are compared with previous methods results. These
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