A Novel Approach to Enhancing Air Pollution Prediction using a Two-Stage Neural XG Boost Detection Algorithm

Authors

  • Keesara Sravanthi Department of Information Technology, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, ‎Hyderabad, Telangana, India
  • Viswanathasarma Ch Department of Computer Science and Engineering, GMR Institute of Technology (A)، Rajam, Andhra Pradesh
  • Lolla Kiran Kumar Department of Information Technology, MVGR College of Engineering(A), Vizianagaram, ‎Andhra Pradesh, India
  • Bodduru Keerthana Department of Information Technology, Anil Neerukonda Institute of Technology and Sciences(A), Sangivalasa, Visakhapatnam, ‎Andhra Pradesh, India
  • Bhavya Munukurthi Department of Computer Science and Engineering (AIML&DS), Anil Neerukonda Institute of Technology and Sciences(A), Sangi-valasa, Visakhapatnam, ‎Andhra Pradesh, India
  • Badugu Samatha Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India

How to Cite

Sravanthi , K. ., Ch , V. ., Kumar , L. K. ., Keerthana , B. ., Munukurthi , B. ., & Samatha , B. . (2025). A Novel Approach to Enhancing Air Pollution Prediction using a Two-Stage Neural XG Boost Detection Algorithm. International Journal of Basic and Applied Sciences, 14(1), 99-105. https://doi.org/10.14419/a2299s64

Received date: March 21, 2025

Accepted date: April 21, 2025

Published date: April 24, 2025

DOI:

https://doi.org/10.14419/a2299s64

Keywords:

Air pollution, pollutant forecasting model, Two-Stage Neural Extreme Gradient Boost pollution detection (TSN-XGB) algorithm, Enhanced Linear Adam Optimization (ELAO

Abstract

With the dramatic increase of industry and transportation in modern civilization, air quality monitoring has received a lot of attention. Increased levels of air pollution can hurt the living environment and potentially bring people into harm's way. An accurate and reliable model to forecast air pollutants needs to be developed to reduce air pollution levels and alert the public about upcoming events involving deadly air pollutants. Many researchers have undertaken various strategies to anticipate and reduce air pollution. However, existing prediction systems cannot deliver cost-effective and real-time solutions with sufficient spatial and temporal resolutions of information. In this study, we propose a Two-Stage Neural Extreme Gradient Boost pollution detection (TSN-XGB) algorithm. Initially, we collect the dataset and perform preprocessing using normalization and spatial-temporal feature extraction. We use the proposed Two-Stage Neural Extreme Gradient Boost pollution detection (TSN-XGB) and Enhanced Linear Adam Optimization (ELAO) for prediction. The MATLAB platform evaluates the performance of the suggested approach and compares it with existing methodologies. The newest pollution detection method based on deep learning used an effective proposed algorithm and achieved 93% accuracy.

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How to Cite

Sravanthi , K. ., Ch , V. ., Kumar , L. K. ., Keerthana , B. ., Munukurthi , B. ., & Samatha , B. . (2025). A Novel Approach to Enhancing Air Pollution Prediction using a Two-Stage Neural XG Boost Detection Algorithm. International Journal of Basic and Applied Sciences, 14(1), 99-105. https://doi.org/10.14419/a2299s64

Received date: March 21, 2025

Accepted date: April 21, 2025

Published date: April 24, 2025