Air Quality Prediction using Random Forest algorithm

As a result of lifestyle choices, industry, and growing urbanisation, pollution in the atmosphere has spread throughout the world and is now a serious threat to human existence. One of the air contaminants, particulate matter, is known to contribute to a variety of illnesses, including cardiovascula...

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Bibliographic Details
Published in:	2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) pp. 1 - 5
Main Authors:	Renugadevi, R., Vyshnavi, Tummala, Reddy, Tadi Prasanna, Lahari, Pothuri Sai
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2023
Subjects:	Air pollution Atmospheric modeling Bi-variate analysis Machine learning algorithms Multivariate analysis Prediction algorithms Pulmonary diseases Random Forest Sensors SMLT Univariate analysis Urban areas
Online Access:	Get full text
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Summary:	As a result of lifestyle choices, industry, and growing urbanisation, pollution in the atmosphere has spread throughout the world and is now a serious threat to human existence. One of the air contaminants, particulate matter, is known to contribute to a variety of illnesses, including cardiovascular and respiratory diseases. To safeguard people from the negative impacts of air pollution in advance, accurate assessment of air pollution concentrations is required. A variety of variables influence air pollution, including weather conditions and the concentration of other contaminants in urban areas. The purpose is to diagnose machine learning-based solutions for predicting air pollution findings as accurately as possible. The dataset is analysed using the supervised machine learning approach (SMLT), which collects a wide range of data, including variable identification and results from univariate, bivariate, and multivariate analyses. We compare and evaluate the performance measures of several machine learning methods on the provided pollution dataset using evaluation approaches.
DOI:	10.1109/RMKMATE59243.2023.10369180