Authors: Assistant Professor R Srinivas, Koppula Sneha, Devadasu Aswini, Gattupalli Ekavani Madhur, Pusuluri Surekha
Abstract: Machine Learning-based Gas Leak Detection and Prevention System operates with intelligent and automated methods to detect and prevent gas leakage occurrences in industrial and domestic situations. Existing detection systems have primarily utilized fixed threshold values for such checks, leading to the most effective method for interpreting false alerts and ineffective response times. The proposed system couples sensor components with an ML algorithm method to processes more productive patterns determined for gas releases while using devices to eliminate these differences. Data is acquired from gas sensors, standard MQ-series sensors, to measure LPG, methane, and carbon monoxide. Real-time data is acquired and processed after processing and analysed by machine learning algorithms, like Support Vector Machine SVC, Random Forest to classify conditions as safe or fallacious. An alarm sounds and IoT sends users alerts such as gas shut-off valves and exhaust fans. When gas becomes available, this ML approach impro
