An IoT based forest fire detection system using integration of cat swarm with LSTM model

Abstract

The destruction of millions of acres of forest each year by forest fires is a global environmental crisis that has real-world consequences for people's livelihoods and the health of our planet. The ability to foresee the onset of such a natural disaster is, thus, of paramount importance in reducing this risk. There have been numerous proposed technologies and novel approaches for detecting and preventing forest fires. Integrating AI to automate fire prediction and detection is becoming increasingly common. To provide effective forest fire detection, people make use of several technological expansions, with the IoT for data collecting and Artificial Intelligence (AI) for the forecast process. Artificial intelligence (AI) is a key study technique that has been proven to be the best in enhancing the presentation of detecting fire threats in important locations by several researchers. Due to the importance of object detection in this investigation, EfficientDet was chosen for implementation. It is suggested that fire breakouts be detected using a Recurrent LSTM Neural Network (RLSTM-NN). Here, we propose a Cat Swarm Fractional Calculus Optimization (CSFCO) algorithm for deep learning that combines the best features of Cat Swarm Optimization (CSO) with fractional calculus for optimal training results (FC). Terms of the simulation results reveal that the suggested process outdoes the state-of-the-art approaches. The suggested typical can identify the onset of a fire with a precision of 98.6% and an error rate of only 0.14%.