Rapid prediction of wildfire spread using ensemble Kalman filter and polyline simplification

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 computa-tional 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.

Automated summary

This paper proposes a new method that combines computational wildfire simulations and ensemble Kalman filter for rapid prediction of wildfire spread. The method uses a two-dimensional polyline simplification algorithm to represent the wildfire perimeter and relates the prediction results with actual observation data. The proposed method is tested and demonstrated using an example wildfire spread scenario, showing that it can reduce computational time while maintaining prediction accuracy. It is expected to be a core algorithm for near-real-time prediction and data-driven updating of wildfire spread.