Forest Fire Detection using Convolutional Neural Network Model

Abstract

Everyone recalls the destruction brought on by the Australian forest fires in 2019. Even though there isn't much we can do to battle forest fires on our own, we can always rely on technology. By this we are trying to predict the accuracy of these models on forest fire data set. We are trying to detect forest fire in dense forest; our data set is very diverse and consist of images having forest fires, smokes, non-smoke and fire images. We have found out that Sensor detection and real-time geological data analysis are two methods for detecting forest fires. However, using image classification, for which Deep learning is the most efficient solution, is one of the best methods for detecting fire. In addition, these algorithms can be integrated with drones using deep learning techniques so that images can be taken frequently from the sky with ease, smoke can be detected in dense forests, and the authorities can be notified to take immediate action. The convolutional neural network algorithm for fire detection was the sole focus of our paper. The value of various epochs is used to evaluate these results.