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Research Article | Open Access

Adaptive path planning for arriving at firelines in dynamic wildfires and complex landscapes

Zeren Gesang1, Bo Yu1,2, Jiasong Zhu3,4,5,6, Wenyu Jiang3,4,5,6()
Transportation Department of Tibet Autonomous Region, Tibet 850001, China
Tibet Transportation Development Group, Tibet 850001, China
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Institute of Urban Smart Transportation & Safety Maintenance, Shenzhen University, Shenzhen 518060, China
National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment, Shenzhen University, Shenzhen 518060, China
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China
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Abstract

Timely deployment of firefighting efforts during the early stages of wildfires is essential to minimize disaster losses. However, complex landscapes and dynamic wildfire behaviors pose significant challenges to firefighters’ rapid arrival at firelines. This paper introduces an adaptive path planning model designed to provide optimal routes in such dynamic environments. A “wildfire–environment–personnel” (WEP) framework was proposed to capture the spatiotemporal interactions among key elements of wildfire scenarios. By integrating a wildfire spread model and a firefighter travel model, the framework simulates the dynamic interplay between wildfires and personnel in wildland settings. A WEP-based model was tailored to calculate adaptive routes from the starting point to the dynamic fireline. Model validation was conducted with a wildfire-prone area in Foshan city as the study site. In an experimental scenario, our model demonstrated a significant advantage over traditional methods, reducing arrival time by more than 60% and accurately identifying the dynamic intersection point of the fireline. These findings underscore the model’s potential for practical application in real-world wildfire suppression and rescue operations.

Keywords

wildfire hazardfire suppressionroute planningadaptive modelingemergency response

References

[1]
Alexander, M. E., Baxter, G. J., Dakin, G. R. 2005. Travel rates of Alberta wildland firefighters using escape routes. In: Proceedings of the 8th International Wildland Fire Safety Summit: Human Factors-Ten Years Later, Missoula, MT, USA, 12.
[2]

Andrews, P. L., Loftsgaarden, D. O., Bradshaw, L. S. 2003. Evaluation of fire danger rating indexes using logistic regression and percentile analysis. International Journal of Wildland Fire, 12: 213.

Crossref Google Scholar
[3]

Anguelova, Z., Stow, D. A., Kaiser, J., Dennison, P. E., Cova, T. 2010. Integrating fire behavior and pedestrian mobility models to assess potential risk to humans from wildfires within the U.S.–Mexico border zone. The Professional Geographer, 62: 230–247.

Crossref Google Scholar
[4]

Batista, M. M., Paludo, A. C., Gula, J. N., Pauli, P. H., Tartaruga, M. P. 2020. Physiological and cognitive demands of orienteering: A systematic review. Sport Sciences for Health, 16: 591–600.

Crossref Google Scholar
[5]

Beer, T. 1991. The interaction of wind and fire. Boundary-Layer Meteorology, 54: 287–308.

Crossref Google Scholar
[6]

Bowd, E. J., Banks, S. C., Strong, C. L., Lindenmayer, D. B. 2019. Long-term impacts of wildfire and logging on forest soils. Nature Geoscience, 12: 113–118.

Crossref Google Scholar
[7]

Butler, B. W. 2014. Wildland firefighter safety zones: A review of past science and summary of future needs. International Journal of Wildland Fire, 23: 295.

Crossref Google Scholar
[8]

Butler, B. W., Cohen, J. D. 1998. Firefighter safety zones: A theoretical model based on radiative heating. International Journal of Wildland Fire, 8: 73.

Crossref Google Scholar
[9]
Butler, B. W., Cohen, J. D., Putnam, T., Bartlette, R. A., Bradshaw, L. S. 2000. A method for evaluating the effectiveness of firefighter escape routes. In: Proceedings of the 4th International Wildfire Safety Summit, Edmonton, AB, Canada, 42–53.
[10]

Campbell, M. J., Dennison, P. E., Butler, B. W. 2017a. A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. International Journal of Wildland Fire, 26: 884.

Crossref Google Scholar
[11]

Campbell, M. J., Dennison, P. E., Butler, B. W. 2017b. Safe separation distance score: A new metric for evaluating wildland firefighter safety zones using lidar. International Journal of Geographical Information Science, 31: 1448–1466.

Crossref Google Scholar
[12]

Campbell, M. J., Dennison, P. E., Butler, B. W., Page, W. G. 2019a. Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied Geography, 106: 93–107.

Crossref Google Scholar
[13]

Campbell, M. J., Dennison, P. E., Thompson, M. P., Butler, B. W. 2022. Assessing potential safety zone suitability using a new online mapping tool. Fire, 5: 5.

Crossref Google Scholar
[14]

Campbell, M. J., Page, W. G., Dennison, P. E., Butler, B. W. 2019b. Escape route index: A spatially-explicit measure of wildland firefighter egress capacity. Fire, 2: 40.

Crossref Google Scholar
[15]

Creagh, U., Reilly, T. 1997. Physiological and biomechanical aspects of orienteering. Sports Medicine, 24: 409–418.

Crossref Google Scholar
[16]

Dwyer, G. 2022. Enacting safety: Firefighter sensemaking of entrapment in an Australian bushfire context. International Journal of Disaster Risk Reduction, 68: 102697.

Crossref Google Scholar
[17]
European Space Agency (ESA). 2023. Counting wildfires across the globe. Available at https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-3/Counting_wildfires_across_the_globe
[18]
Fan, C. S. 2021. Public Safety and Emergency Management. Science Press.
[19]
Finney, M. A. 1998. FARSITE: Fire Area simulator - Model development and evaluation. Research Paper RMRS-RP-4. Ogden, UT, USA: USDA Forest Service, Rocky Mountain Research Station.
Crossref
[20]

Finney, M. A. 2002. Fire growth using minimum travel time methods. Canadian Journal of Forest Research, 32: 1420–1424.

Crossref Google Scholar
[21]

Frost, S. M., Alexander, M. E., Jenkins, M. J. 2022. The application of fire behavior modeling to fuel treatment assessments at army garrison camp williams, Utah. Fire, 5: 78.

Crossref Google Scholar
[22]

Fryer, G. K., Dennison, P. E., Cova, T. J. 2013. Wildland firefighter entrapment avoidance: Modelling evacuation triggers. International Journal of Wildland Fire, 22: 883.

Crossref Google Scholar
[23]
Geospatial Data Cloud (GDC). 2024. ASTER GDEM 30M. Available at https://www.gscloud.cn/sources/accessdata/310?pid=302
[24]

Gharakhanlou, N. M., Hooshangi, N. 2021. Dynamic simulation of fire propagation in forests and rangelands using a GIS-based cellular automata model. International Journal of Wildland Fire, 30: 652–663.

Crossref Google Scholar
[25]
Gleason, P. 2017. Lookouts, communications, escape routes, and safety zones. Available at https://www.fireleadership.gov/toolbox/documents/lces_gleason.html
[26]

Godfree, R. C., Knerr, N., Encinas-Viso, F., Albrecht, D., Bush, D., Christine Cargill, D., Clements, M., Gueidan, C., Guja, L. K., Harwood, T., et al. 2021. Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation. Nature Communications, 12: 1023.

Crossref Google Scholar
[27]
Gollapudi, S., Kollias, K., Panigrahi, D. 2022. The pit stop problem: How to plan your next road trip. In: Proceedings of the 30th International Conference on Advances in Geographic Information Systems, Seattle Washington, USA, 1–9.
Crossref
[28]
Google. 2024. Google Earth. Available at https://earth.google.com/web/
[29]

Hansen, R. 2012. Estimating the amount of water required to extinguish wildfires under different conditions and in various fuel types. International Journal of Wildland Fire, 21: 525.

Crossref Google Scholar
[30]

Hilton, J., Garg, N. 2021. Rapid wind–terrain correction for wildfire simulations. International Journal of Wildland Fire, 30: 410–427.

Crossref Google Scholar
[31]
Intergovernmental Panel on Climate Change (IPCC). 2021. Climate change 2021: The physical science basis. Available at https://www.ipcc.ch/report/ar6/wg1/#SPM
[32]
Jiang, W. Y., Wang, F., Su, G. F., Li, X., Meng, Q. X., Wang, G. N. 2022a. Key technologies of emergency management informatization for forest fires. China Safety Science Journal, 32 (9): 182–191. (in Chinese)
[33]

Jiang, W., Qiao, Y., Su, G., Li, X., Meng, Q., Wu, H., Quan, W., Wang, J., Wang, F. 2023. WFNet: A hierarchical convolutional neural network for wildfire spread prediction. Environmental Modelling & Software, 170: 105841.

Crossref Google Scholar
[34]

Jiang, W., Qiao, Y., Zheng, X., Zhou, J., Jiang, J., Meng, Q., Su, G., Zhong, S., Wang, F. 2024. Wildfire risk assessment using deep learning in Guangdong Province, China. International Journal of Applied Earth Observation and Geoinformation, 128: 103750.

Crossref Google Scholar
[35]

Jiang, W., Wang, F., Fang, L., Zheng, X., Qiao, X., Li, Z., Meng, Q. 2021. Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model. Environmental Modelling & Software, 135: 104895.

Crossref Google Scholar
[36]

Jiang, W., Wang, F., Su, G., Li, X., Wang, G., Zheng, X., Wang, T., Meng, Q. 2022b. Modeling wildfire spread with an irregular graph network. Fire, 5: 185.

Crossref Google Scholar
[37]
Kozlov, M. 2021. How record wildfires are harming human health. Nature. Available at https://www.nature.com/articles/d41586-021-03496-1
[38]
LANDFIRE. 2023. 40 Scott and Burgan fire behavior fuel models. Available at https://www.landfire.gov/fbfm40.php
[39]

Liu, N., Lei, J., Gao, W., Chen, H., Xie, X. 2021. Combustion dynamics of large-scale wildfires. Proceedings of the Combustion Institute, 38: 157–198.

Crossref Google Scholar
[40]
Mandel, J., Beezley, J. D., Kochanski, A. K. 2011. Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011. Geoscientific Model Development, 4 : 591–610.
Crossref
[41]

Mastorakos, E., Gkantonas, S., Efstathiou, G., Giusti, A. 2023. A hybrid stochastic Lagrangian–cellular automata framework for modelling fire propagation in inhomogeneous terrains. Proceedings of the Combustion Institute, 39: 3853–3862.

Crossref Google Scholar
[42]
National Bureau of Statistics (NBS). 2024. China statistical yearbook. Available at https://www.stats.gov.cn/sj/ndsj/
[43]
National Interagency Fire Center (NIFC). 2024. National fire news. Available at https://www.nifc.gov/fire-information/nfn
[44]
United Nations Environment Programme (UNEP). 2022. Spreading like wildfire: The rising threat of extraordinary landscape fires. Report. Available at https://www.unep.org/resources/report/spreading-wildfire-rising-threat-extraordinary-landscape-fires
[45]

Soule, R. G., Goldman, R. F. 1972. Terrain coefficients for energy cost prediction. Journal of Applied Physiology, 32: 706–708.

Crossref Google Scholar
[46]

Sullivan, P. R., Campbell, M. J., Dennison, P. E., Brewer, S. C., Butler, B. W. 2020. Modeling wildland firefighter travel rates by terrain slope: Results from GPS-tracking of type 1 crew movement. Fire, 3: 52.

Crossref Google Scholar
[47]
Tobler, W. 1993. Three presentations on geographical analysis and modeling. Techical Report. University of California, Santa Barbara, USA.
[48]

Van der Velde, I. R., van der Werf, G. R., Houweling, S., Maasakkers, J. D., Borsdorff, T., Landgraf, J., Tol, P., van Kempen, T. A., van Hees, R., Hoogeveen, R., et al. 2021. Vast CO2 release from Australian fires in 2019–2020 constrained by satellite. Nature, 597: 366–369.

Crossref Google Scholar
[49]
Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., Liu, W. 2023. GWL_FCS30: A global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020. Earth System Science Data, 15 : 265–293.
Crossref
[50]

Zhou, T., Ding, L., Ji, J., Li, L., Huang, W. 2019. Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method. Fire Safety Journal, 105: 95–106.

Crossref Google Scholar
Safety Emergency Science
Volume 1 Issue 1,
March 2025
Article number: 9590004
DOI: 10.26599/SES.2025.9590004
Cite this article:
Gesang Z, Yu B, Zhu J, et al. Adaptive path planning for arriving at firelines in dynamic wildfires and complex landscapes. Safety Emergency Science, 2025, 1(1): 9590004. https://doi.org/10.26599/SES.2025.9590004
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εv under different slopes and land covers

SlopeLand cover
Barren landBrushTimberWater
Flat (≤ 10°)1.00.50.2500.003
Moderate (10°–30°)1.00.50.167—
Steep (≥ 30°)1.00.50.100—