FOREST FIRE ANALYSIS USING MACHINE LEARNING

Abstract

Forest fires have become a major threat worldwide, causing many negative impacts on human habitats and forest ecosystems. Climate change and the greenhouse effect are some of the consequences of this destruction. Interestingly, the rate of forest fires occurring due to human activities is higher. Therefore, in order to minimize the devastation caused by forest fires, it is necessary to detect forest fires at an early stage. This paper proposes a system and method that can be used to detect forest fires at an early stage using a CNN model. Detecting smoke and fire is a difficult task, because variations in color and texture are so whimsical. Many smoke and fire image classification methods have been proposed to overcome this problem, but most of them are rule-based methods where accuracy is lower or homemade methods are expensive to produce. In this paper, we propose a new fire detection system using convolutional neural network. Fire detection using machine learning. It can be extremely difficult to detect fire and smoke with sensors already installed in buildings. They are slow and unprofitable due to their rudimentary design and technology. To solve this problem caused by training the network on a limited dataset, we improve the number of training images available using traditional techniques of data usage and enhancement.