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August 1, 2025Journal of Informatics Education and Research

Predictive modeling of air pollution levels: A state-of-the-art review of machine learning techniques

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Authors

MGMr. Mayur Dutta Ms. Reena G.Bhati

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Overview

Survey reveals state-of-the-art machine learning approaches for air quality forecasting, indicating potential research areas.

Key Points

  • Main finding highlights the need for accurate forecasting methods to address air pollution.
  • Key evidence points to various machine learning techniques, including artificial intelligence and deep learning models.
  • Approach includes a comprehensive review of data sources, preprocessing techniques, and core algorithms used in predicting air quality.
  • Significance lies in identifying challenges and research gaps that can guide future studies in air quality prediction.

Cite This Study

Mr. Mayur Dutta Ms. Reena G.Bhati (2025) studied this question.

synapsesocial.com/papers/689a0c65e6551bb0af8cfa1dhttps://doi.org/10.52783/jier.v5i3.3330
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Survey on Air Pollution Prediction Using Machine Learning Techniques2025
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  4. 4Air Predictive Modeling for Air Quality: A Comparative Study of Machine Learning and Deep Learning Techniques2024
  5. 5A systematic survey of air quality prediction based on deep learning2024 · 31 citations