Advanced Machine Learning Model for Hazardous Gas Detection and Early Warning

The increasing release of hazardous gases such as carbon monoxide, methane, ammonia, sulfur dioxide, and nitrogen oxides poses a significant threat to environmental sustainability and public health, particularly in rural and remote regions where industrial safety infrastructure and continuous monitoring facilities are often limited or entirely absent.Traditional gas monitoring systems in such regions rely heavily on periodic manual inspections or threshold-based alarm mechanisms, which are insufficient for early detection, trend analysis, and proactive risk mitigation.This research proposes a machine learning-based platform designed to continuously monitor, analyze, and predict hazardous gas concentrations in rural and geographically isolated areas using low-cost sensors and intelligent data-driven models.The proposed platform integrates real-time environmental data acquisition with advanced predictive analytics to identify abnormal gas patterns before they reach critical levels.A Support Vector Machine-based approach is first analyzed as an existing solution due to its effectiveness in classification problems.However, its limitations in handling temporal dependencies and sequential gas concentration data motivate the adoption of a Long Short-Term Memory-based deep learning model.The LSTM architecture enables accurate time-series prediction by learning long-term dependencies within sensor data, making it particularly suitable for forecasting hazardous gas trends.The system aims to enhance early warning capabilities, reduce human exposure risks, and support decisionmaking for emergency response and rural health authorities.The outcomes of this study demonstrate that machine learning-driven monitoring platforms can significantly improve prediction accuracy, scalability, and reliability compared to conventional methods, offering a practical and sustainable solution for environmental safety in underserved regions.