AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG

An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

Automated summary

This paper proposes an automatic framework for leakage detection and consequence prediction of liquefied petroleum gas (LPG) during transportation using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident, and an IoT framework is proposed for gas leakage detection and reporting to the disaster management team. The proposed solution allows for quick detection and prediction of gas leakage, aiding in efficient disaster management.