Authors : Nikhil Sanjay Suryawanshi

Volume/Issue : Volume 5 - 2020, Issue 5 - May

Google Scholar : https://tinyurl.com/5ysac72e

Scribd : https://tinyurl.com/2p9ant74

DOI : https://doi.org/10.38124/ijisrt/IJISRT20MAY999

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing concern for environmental health has led to a heightened need for accurate air quality monitoring and prediction. This study presents a framework for predicting the Air Quality Index (AQI) using existing datasets rather than relying on real-time data from IoT sensors. The proposed system incorporates various machine learning algorithms, including Linear Regression, Neural Networks, and XGBoost, to analyze the relationships between air pollution indicators and AQI values. The methodology encompasses essential steps such as data preprocessing, normalization, and dividing the dataset into training and testing sets. Although the system has not yet been implemented, preliminary analyses indicate that the use of these models has the potential to yield reliable AQI predictions, which can significantly assist policymakers and public health officials in implementing effective air quality management strategies.

Keywords : IoT Sensors, Air Quality Index (AQI), Machine Learning, Deep Learning, Random Forest, Linear Regression, Neural Networks, XGBoost, Logistic Regression, Voting Classifier, AQI Values, Min-Max Scaling.

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