A Machine Learning-Based Platform for Monitoring and Prediction of Hazardous Gases in Rural and Remote Areas
The emissions of pollutants and radioactive gases are the main causes of several environmental disasters that may cause premature deaths. The significant impact of these gases on public health is a major concern, especially in remote and rural regions. In these areas, access to security and health s...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10855430/ |
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| Summary: | The emissions of pollutants and radioactive gases are the main causes of several environmental disasters that may cause premature deaths. The significant impact of these gases on public health is a major concern, especially in remote and rural regions. In these areas, access to security and health services is scarce, and real-time monitoring of citizens and the conditions in which they live is very difficult. Without means to monitor or predict, healthcare and government stakeholders typically act too late when indoor incidents occur. Hence, this paper presents a digital decision support system that uses Machine Learning (ML) for monitoring and prediction of incidents related with indoor hazardous gases. This system is implemented on top of an Internet of Things (IoT) ecosystem named RuraLTHINGS. This project, developed by the University of Beira Interior, Portugal, monitors the quality of air in remote and rural regions in real-time. The platform aims to predict and notify residents and other stakeholders about environmental conditions and prevent the risk of exposure to dangerous gases. The system uses ML techniques to analyze the collected data and provide future predictions using unidirectional Long Short-Term Memory (LSTM) layers overlaid on bidirectional LSTM layers, meaning layers stacked together, which was the model architecture that delivered the best results in this context. This paper presents the validation of the digital platform and the ML model using a real test bed environment. The model successfully predicted future data trends related to indoor monitoring of hazardous gases. |
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| ISSN: | 2169-3536 |