A forest firefighting task area division method based on the SW-DBSCAN algorithm

Abstract

Abstract A new method is proposed for generating forest fire-fighting tasks using an improved Sliding Window-based Weighted Density-Based Spatial Clustering of Applications with Noise (SW-DBSCAN). To reduce the neighborhood search range of core objects and improve the clustering accuracy of key protected object, fire head, fire tail, and fire flank on the fire line, a sliding window is introduced, and a weighted similarity distance measurement method is designed in the DBSCAN framework. At the same time, evaluation indicators are proposed to evaluate the effectiveness of this method. Finally, several numerical simulations show that this method is feasible and flexible for generating forest firefighting tasks.