Forest Fire Risk Assessment and Detection using Deep Learning Models

Background: There is a severe need to detect any kind of fire in a faster and accurate method, especially forest fires to stop huge losses to the human community and the environment losses. The main purpose of the proposal is to identify and evaluate the accuracy of the existing Artificial Intelligence (AI) methods for detecting fire and improve the methods to detect fire in real-world scenarios in faster and accurate methods. Methods: The proposal uses a dataset to train a model, and in addition uses a few test images from an existing database to test the models developed. We develop and test the following neural network models namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and a combination of RNN and CNN. Findings: A conservative estimate of the yearly losses caused by forest fires in India for the entire nation is 440 crores INR. The loss of biodiversity, soil moisture, nutrients, and other intangible advantages is not factored in this assessment. The proposed models namely DNN, RNN, CNN, and RNN+CNN give an accuracy of 55%, 61%, 55% and 98% respectively. Novelty and applications: The RNN+CNN model proposed to have good accuracy which is much better compared to existing models. The model in addition can be used in real-time CCTV surveillance which can predict the fire in real-time with a faster alert duration of less than 30 sec. Keywords: Fire detection, CNN, RNN, DNN, Artificial Intelligence (AI), Environment loss, Fire Losses