Estimation of the Post-burning Area of the Fire Hazard Severity Zone in California from Landsat 8 OLI Images Using Deep Learning Machine Intelligence Model

A forest fire is an unplanned and unexpected event that contaminates our environment by burning the grass, trees, and vegetables of any forest and have an impact on the country’s economic model and the functioning of our ecosystem. A forest fire begins as a result of a natural occurrence, human activities, global warming, or lightning strikes. California is one of them that is affected by fires everyday. However, the statistics and procedures needed to reliably characterize fire propagation, behavior, and consequences are still lacking. As a result, our understanding of wildfire behavior remains restricted. In this paper, we have analyzed the area affected by the fire of August 7, 2016, with the help of machine intelligence deep learning model from Landsat 8 OLI images. The model has a total of five hidden layers, and the model has been trained with seven input features. The accuracy of the model has been obtained as 99.4712% with a baseline error of 0.53%. The area affected by a large fire to the north of San Bernardino in the state of California (USA) is 171.730934 km2 (42,435.637956 Acres). We used this approach to map California’s fire history in August 2016.