An adaptive quadtree-based approach for efficient decision making in wildfire risk assessment. (February 2023)

• Record Type: Journal Article
• Authors: KC, Ujjwal; Garg, Saurabh; Hilton, James; Aryal, Jagannath
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Fire management -- High fire-risk areas -- Quadtree -- Fire simulation tool -- Spark -- Wildfire behavior prediction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105590
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Rapidly identifying high-risk areas for potential wildfires is crucial for preparedness, disaster management, and operational logistics decisions. With the advancement of technologies such as Cloud computing, high-risk areas can be determined ahead of time by simulating several possible fires based on forecast conditions. However, such systems may take longer and delay decision-making. We introduce a novel approach that harnesses the benefits of quadtree-based search strategies and conditional probability to enable rapid identification of high fire-risk areas and produces an increasingly detailed risk map within a given time frame. We also present a comprehensive performance analysis of different search strategies to investigate the trade-off between risk areas coverage and time efficiency showcasing how decision-makers can modify parameters based on time requirements. Experimental results show that up to 80% of high fire-risk areas in Tasmania can be identified with the proposed approach in about 20% less time than conventional comprehensive sweep methods. Highlights: We proposed an adaptive search approach for rapid risk identification without running simualations at all start locations. We demonstrated the efficacy of the proposed approach in terms of time efficiency and coverage with its application to Tasmania. We validated the usefulness of the proposed approach for prioritization of response activities in operational wildfire management.