QRNN with Addax Wolf Bird Optimization-based LPG gas leakage detection and alert system with IoT edge computing

Abstract

Liquefied Petroleum Gas (LPG) is widely used in households, commercial establishments industrial sectors. LPG is highly flammable and poses a risk of explosion if not handled properly. Hence, an early gas leakage detection system is crucial for ensuring safety in various environments. In this article, a Deep Learning (DL)-based LPG Gas leakage detection and Alert system named Quasi-Recurrent Neural Network with Addax Wolf Bird Optimization Algorithm (QRNN_AWBOA) is proposed using the gas leakage data. In this approach, the median normalization method is utilized to normalize the raw data. Then, a fusion model named Deep Neural Network (DNN) with Neyman similarity is utilized for the feature fusion process. Then, the data is augmented using oversampling. Later, the detection process is carried out using the Quasi-Recurrent Neural Network (QRNN) model. The QRNN effectively trained the Addax Wolf Bird Optimization Algorithm (AWBOA). Finally, an alert is sent to the NG112 authorities if the leakage is present. This detection system attained the lowest Mean Squared Error (MSE) of 0.016, Mean Absolute Percentage Error (MAPE) of 0.056, Root Mean Squared Error (RMSE) of 0.128, and Weighted Absolute Percentage Error (WAPE) of 0.057.