Integrated Environmental Pollution Monitoring and Prediction Platform

Author

Ankita V Vittalker, Gireesh L P, Kavana U M, Keerthana V Kumar, Niveditha T Naik


Abstract

 Air, water, and soil pollution are rising sharply across rural, urban and industrial regions, yet the systems used to track them remain fragmented, sparse, and reactive rather than predictive. This paper presents the Integrated Environmental Pollution Monitoring and Prediction Platform (IEPMP), a system that combines IoT-based multi-media sensing with machine learning to give both a real-time picture of environmental conditions and a short-term forecast of how they are likely to change.

The proposed system captures air, water, and soil parameters using low-cost sensors, transmits them through Wi-Fi-enabled microcontrollers to a cloud backend, and applies regression and instance-based learning models to forecast near-term pollution levels. It presents essential information such as pollutant concentration, safety status, predicted trend, and location through a simple, colour-coded dashboard.

The system aims to reduce the response gap between when pollution occurs and when it is acted upon, by combining continuous sensing with predictive alerts. It also provides a foundation for further features such as cross-media correlation analysis, deep learning-based forecasting, and city-wide scalability to improve environmental management efficiency.



Keywords

Environmental Pollution, IoT Sensing, Air Quality, Water Quality, Soil Health, Machine Learning, Pollution Prediction, Decision Support.



Full Text:

Download Paper PDF


References


  1. Kalajdjieski, K. Trivodaliev, G. Mirceva, S. Kalajdziski and S. Gievska, "A Complete Air Pollution Monitoring and Prediction Framework," in IEEE Access, vol. 11, pp. 88730-88744, 2023, doi: 10.1109/ACCESS.2023.3251346
  2. Sowmiya and P. Sowmiya, "Industrial Environmental Pollution Monitoring and Prediction Analysis Using IoT and Machine Learning," 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2023, pp. 1854-1859, doi: 10.1109/ICECA58529.2023.10395276.
  3. Rakib, S. Haq, M. I. Hossain and T. Rahman, "IoT Based Air Pollution Monitoring & Prediction System," 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, 2022, pp. 184-189, doi: 10.1109/ICISET54810.2022.9775871.
  4. Johar, S. A. Lahari and P. RN, "Water Quality Index Prediction System Using IoT Enabled Sensor Suite and Machine Learning," 2023 16th International Conference on Sensing Technology (ICST), HYDERABAD, India, 2023, pp. 1-6, doi: 10.1109/ICST59744.2023.10460817.
  5. Leonila, G. A. Senthil, S. Geerthik, R. Sowmiya and J. Nithish, "Dynamic Water Quality Monitoring via IoT Sensor Networks and Machine Learning Technique," 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2024, pp. 1-6, doi: 10.1109/IC3IoT60841.2024.10550224.
  6. Zhang, D. Wen, Y. Liu, Z. Ma, B. Li and X. Ma, "Intelligent Monitoring and Management of Soil Quality based on Deep Learning Algorithm," 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE), Bengaluru, India, 2025, pp. 1-6, doi: 10.1109/ICICKE65317.2025.11136339
  7. Catovic, E. Kadusic, C. Ruland, N. Zivic and N. Hadzajlic, "Air pollution prediction and warning system using IoT and machine learning," 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives, 2022, pp. 1-4, doi: 10.1109/ICECCME55909.2022.9987957.
  8. A. Rahu, A. F. Chandio, K. Aurangzeb, S. Karim, M. Alhussein and M. S. Anwar, "Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality," in IEEE Access, vol. 11, pp. 101055-101086, 2023, doi:10.1109/ACCESS.2023.3315649.

 

Share your valuable work from Social Media Buttons