A Machine Learning-Based Platform For Monitoring And Prediction Of Hazardous Gases In Rural And Remote Areas
DOI:
https://doi.org/10.46647/rdems0204039Keywords:
Machine Learning, Internet of Things (IoT), Hazardous Gas Detection, Gas Prediction, Environmental Monitoring, Rural Areas, Remote Sensing, Air Pollution, Real-Time Monitoring, Data Analytics, Predictive Modeling, Sensor Networks, Smart Environment, Early Warning SystemAbstract
Monitoring hazardous gases in rural and remote areas is essential for ensuring environmental safety and protecting public health, yet these regions often lack reliable infrastructure and real-time monitoring systems. This project proposes a machine learning-based platform that integrates IoT-enabled gas sensors to continuously collect data on harmful gases such as carbon monoxide (CO), methane (CH₄), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). The collected data is transmitted to a centralized system where it undergoes preprocessing and analysis to identify patterns and detect abnormal conditions.
The system employs advanced machine learning algorithms to predict future gas concentration levels and assess potential risks. Based on these predictions, an automated alert mechanism is triggered to notify users and authorities through web or mobile interfaces, enabling timely preventive actions. The proposed solution is scalable, cost-effective, and suitable for deployment in resource-constrained environments. Overall, it enhances the accuracy of gas monitoring, provides early warning capabilities, and contributes to improved environmental management and public safety.