Abstract
The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detec-tion and alarming system using deep learning (IWFFDA-DL) model. The pro-posed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Inte-grated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short term memory (ACNN-BLSTM) model is applied to examine and identify the existence of danger. For hyperpara-meter tuning of the ACNN-BLSTM model, the bacterial foraging optimization (BFO) algorithm is employed and thereby enhances the detection performance. Finally, when the fire is detected, the Global System for Mobiles (GSM) modem transmits messages to the authorities to take required actions. An extensive set of simulations were performed and the results are investigated interms of several aspects. The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.