Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This study addresses wildfire resource prepositioning in a region. Location analytics is utilized for prepositioning decision-making, relying on bi-objective spatial optimization to identify a strategic location for monitoring and response that considers two objectives: optimizing the visibility of potential ignition sites and optimizing response access. The study broadens the conventional definition of coverage by incorporating visibility from the preposition. Despite the inherent complexity of the problem, the proposed solution approach guarantees the identification of the complete set of Pareto optimal solutions in an efficient manner, supporting real-time decision-making. This research contributes significantly to spatial decision-making by integrating viewshed analysis into location analytics. Additionally, the approach has broad applications in various domains, including facility location planning, wireless network design, environmental monitoring, and urban planning. To facilitate decision-making, an interactive web application (www.wri-ucsb.com:8000) is developed, allowing the selection of specific regions of interest and model parameter refinement.
Recommended Citation
Baik, Jiwon and Murray, Alan, "Location Analytics to Support Wildfire Response Prepositioning" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 5.
https://aisel.aisnet.org/hicss-57/li/research/5
Location Analytics to Support Wildfire Response Prepositioning
Hilton Hawaiian Village, Honolulu, Hawaii
This study addresses wildfire resource prepositioning in a region. Location analytics is utilized for prepositioning decision-making, relying on bi-objective spatial optimization to identify a strategic location for monitoring and response that considers two objectives: optimizing the visibility of potential ignition sites and optimizing response access. The study broadens the conventional definition of coverage by incorporating visibility from the preposition. Despite the inherent complexity of the problem, the proposed solution approach guarantees the identification of the complete set of Pareto optimal solutions in an efficient manner, supporting real-time decision-making. This research contributes significantly to spatial decision-making by integrating viewshed analysis into location analytics. Additionally, the approach has broad applications in various domains, including facility location planning, wireless network design, environmental monitoring, and urban planning. To facilitate decision-making, an interactive web application (www.wri-ucsb.com:8000) is developed, allowing the selection of specific regions of interest and model parameter refinement.
https://aisel.aisnet.org/hicss-57/li/research/5