Ujjwal KC, Saurabh Garg, J. E. Hilton, Jagannath Aryal
Year Published:

Cataloging Information

Fire Behavior
Simulation Modeling

NRFSN number: 25289
FRAMES RCS number: 67042
Record updated: February 6, 2023

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.


KC, Ujjwal; Garg, Saurabh; Hilton, James; Aryal, Jagannath. 2023. An adaptive quadtree-based approach for efficient decision making in wildfire risk assessment. Environmental Modelling & Software 160:105590.

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