LPG Leak Detection and Alert System Based on IoT Using Consequence Prediction Multimodel Data

Liquefied Petroleum Gas (LPG) leakage remains a critical safety concern. Although Internet of Things (IoT)based systems facilitate leakage prediction, their accuracy often falls short. To address this, early detection of gas leakage levels is essential. The proposed system enhances the preprocessing phase by eliminating duplicate and unknown data while retaining valuable information, thereby improving prediction reliability. It further boosts performance by computing weight rates for both minority and majority classes within the dataset. The system enables optimized early risk prevention and supports secure long-term testing of safety mechanisms. It integrates both synchronous and asynchronous data transmission to assess leak size, gas type and environmental conditions. parameters are used for accurate evaluation, testing, validation of leakage predictions the system achieves high reliability, scalability, operational efficiency, while reducing time complexity and power consumption. With an achieved accuracy of 92% proposed model offers a robust and effective solution for real-time gas leakage monitoring.