Assessment and Management of Risk on Account of Forest Fires

A major issue on a global scale is the spread of wildfires. Things will only become worse in the future. Climate change and human activity are the main contributing factors. Forest fires are a serious threat to human life, local flora and fauna and property. The release of harmful gases into the atmosphere, during any wildfire degrades the quality of air. The quick control over forest fires requires the deployment of resources such as firefighting system at appropriate locations. Forest fires may be mapped based on their intensities or risk levels. The risk assessment helps estimate the likelihood of forest fire, its intensity, and effects. Autonomous unmanned aerial vehicles (UAVs), watchtowers, sensors, and base stations must be strategically located to ensure optimal visibility. The conventional methods of forest fire risk assessment face certain challenges as the behaviour of forest fires are nonlinear and complex. Modern techniques such as machine learning (ML) algorithms or models such as random forests, support vector machines, etc., may be used to overcome the challenges faced. The accuracy of these algorithms may be increased through their combinations or integrations with other ML algorithms to give hybrid and ensemble models. The data sets required for risk assessment may be obtained through Geographic information system (GIS) or aerial monitoring. Meteorology and topography are important factors in the risk assessment of forest fires. For information to be accurate, dependable, and timely, satellite observation is essential. Present author reviews the literature and presents a case study on prediction of forest fire risk for the region ‘Dhanaulti Reserved Forest’, Uttarakhand, India. The case study highlights the use of Microsoft Excel® in the analysis of weather data.