Smart Monitoring and Prediction of Industrial Pollution Using IoT and ANN

DOI

10.2991/978-94-6463-716-8_69How to use a DOI?

Keywords

Internet of Things; Machine Learning; Embedded System; Artificial Neural Network

Abstract

The environment suffers greatly from industrial pollution, which emits harmful gases such as sulfur dioxide and carbon monoxide. These emissions lead to the formation of smog, acid rain, and global warming, besides adversely affecting the respiratory system. In comparison, the high concentration of carbon dioxide and other greenhouse gas emissions leads to rising temperatures due to ice caps melting and extreme and violent storms and drought which directly threaten terrestrial life. Its pollution also contaminates drinking water with toxic substances besides industrial machinery noise disrupting normal ecosystems. Chemical spills have also been known to raise environmental damage besides land deterioration that affects biodiversity and results in long- term ecological destabilization. This paper offers a solution for integrating Machine Learning and Internet of Things (IoT) technologies into the MATLAB platform to mitigate pollution monitoring and management. IoT-based technologies make it possible to monitor levels of pollutants in real-time, and comparison with established thresholds sends an alert in case limits are exceeded. Predictive algorithms of machine learning classify the diverse field parameters, identify trends, and forecast future events that may arise due to pollution. This approach will ensure timely prevention, maintaining pollution levels within acceptable limits. The system is intended to reduce overall pollution, safeguard biodiversity and public health, and promote long-term environmental sustainability.

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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TY  - CONF
AU  - S. S. Surekha
AU  - D. M. Swarna Lakshmi
AU  - A. S. NithishBalaji
AU  - N. S. Abinaya
PY  - 2025
DA  - 2025/05/26
TI  - Smart Monitoring and Prediction of Industrial Pollution Using IoT and ANN
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 926
EP  - 941
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_69
DO  - 10.2991/978-94-6463-716-8_69
ID  - Surekha2025
ER  -