Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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AI-Driven Prediction of Diwali Noise Pollution Using Deep and Reinforcement Learning

Authors
Gandroju Mahalakshmi Sree1, *, Mantena Sireesha2, Mantena Siva Pavan Kumar Raju3, Rishyanth Bonguluru1, Abdul Gaffar Sheik4, Purushottama Rao Dasari1, 5
1Department of Chemical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, 534101, Andhra Pradesh, India
2Center for Geospatial and Saline Studies, Sasi Institute of Technology &Engineering, Tadepalligudem, Andhra Pradesh, 534101, India
3Center for Innovation, Incubation & Entrepreneurship, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, 534101, India
4School of Engineering, The University of British Columbia Okanagan, 3333 University Way, Kelowna, BC, V1V 1V7, Canada
5Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada
*Corresponding author. Email: mahalakshmisree22@gmail.com
Corresponding Author
Gandroju Mahalakshmi Sree
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_7How to use a DOI?
Keywords
Noise Pollution; Diwali; Deep Learning; Reinforcement Learning; Geospatial Analysis; Environmental Monitoring
Abstract

Noise pollution during Diwali festivities in India has emerged as a serious concern, affecting public health and the environment. This study proposes an AI-driven framework that integrates deep learning, reinforcement learning, and geospatial analysis to forecast and optimize noise levels during Diwali. Using Central Pollution Control Board (CPCB) data from 2018–2020 across multiple locations, we developed a neural network model for noise prediction and further optimized its performance using the Proximal Policy Optimization (PPO) algorithm. The predictive model achieved high accuracy with an R2 score of 0.9921, along with low RMSE and MAE values, demonstrating robust forecasting ability. Reinforcement learning enhanced the stability and adaptability of predictions, while geospatial modeling identified critical noise hotspots. The results indicate that AI-based methods can significantly aid policymakers and urban planners in devising effective noise control strategies, thereby promoting environmental sustainability and public health during large-scale festive events.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_7How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Gandroju Mahalakshmi Sree
AU  - Mantena Sireesha
AU  - Mantena Siva Pavan Kumar Raju
AU  - Rishyanth Bonguluru
AU  - Abdul Gaffar Sheik
AU  - Purushottama Rao Dasari
PY  - 2025
DA  - 2025/12/31
TI  - AI-Driven Prediction of Diwali Noise Pollution Using Deep and Reinforcement Learning
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 65
EP  - 73
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_7
DO  - 10.2991/978-94-6463-940-7_7
ID  - Sree2025
ER  -