Real-Time Hazardous Gas Detection and Prediction Using Machine Learning Algorithms

Abstract

The environment and humans are adversely affected by the harmful gases that are released by factories, communities, and mines. This paper introduces a real-time hazardous gas monitoring and forecasting system. The detection and prevention of gas incidents are expedited by machine learning. The objective is to employ IoT sensors to monitor hazardous gases, such as carbon monoxide, methane, and LPG. Risk prediction is achieved through the utilization of sophisticated data analytics. In order to generate precise predictions and classifications, the proposed system implements cloud monitoring, gas sensors, and machine learning methodologies such as Support Vector Machine and Random Forest. In order to identify anomalous gas levels and notify authorities, sensor data is consistently collected, processed, and analyzed. Studies have demonstrated the ability to improve forecasts, accelerate reactions, and identify gas escapes in a variety of environmental conditions. The research enhances the safety of environmental and industrial operations by eliminating the need for human intervention, facilitating proactive monitoring, and offering a cost-effective, scalable smart gas monitoring solution.