Rapid prediction of wildfire spread using ensemble Kalman filter and polyline simplification. (February 2023) - Yoo, Seungmin; Song, Junho
Abstract: This paper proposes a new method for rapid prediction of wildfire spread, which employs computational wildfire simulations by FARSITE and assimilates the simulation results with actual observation data by means of an ensemble Kalman filter. To expedite data assimilation, the wildfire perimeter is represented by a two-dimensional polyline simplification algorithm. In addition, to facilitate the data assimilation, a new process is developed to relate the prediction results with the actual observation data. The proposed method is tested and demonstrated by an example wildfire spread scenario generated based on actual climate, topography, and vegetation. The results confirm that the polyline simplification algorithm can drastically reduce the computational time required for data assimilation while maintaining the accuracy of predictions. The proposed method is expected to serve as a core algorithm for near-real-time prediction and data-driven updating of wildfire spread. Highlights: A new rapid prediction algorithm is developed for wildfire spread using EnKF and FARSITE. The proposed algorithm simplifies the wildfire perimeter by applying polyline simplification. To apply EnKF, a new method of reconstructing simplified perimeters is also proposed. The algorithm significantly reduces computation time while maintaining prediction accuracy. The proposed method is successfully demonstrated by wildfire spread scenarios.