An optimization model for aggregation of prescribed burn units

Minas, J and Hearne, J 2016, 'An optimization model for aggregation of prescribed burn units', TOP, vol. 24, no. 1, pp. 180-195.


Document type: Journal Article
Collection: Journal Articles

Title An optimization model for aggregation of prescribed burn units
Author(s) Minas, J
Hearne, J
Year 2016
Journal name TOP
Volume number 24
Issue number 1
Start page 180
End page 195
Total pages 16
Publisher Springer
Abstract Fire is a natural component of many terrestrial ecosystems; however, uncontrolled intense wildfires can cause loss of human life and destruction of natural resources. Prescribed burning is a management activity undertaken for the purposes of both wildfire hazard reduction and the preservation of fire-adapted ecosystems. Achievement of prescribed burn targets is made difficult by operational constraints such as limited personnel, equipment and suitable weather for undertaking burning. Prescribed burn program implementation may benefit from efficiencies gained in undertaking larger burns. Here, we present a mixed-integer programming formulation for aggregation of prescribed burn units. The model minimizes the total prescribed burn perimeter requiring management, by aggregating existing 'fundamental' burn units into larger compact and contiguous units or 'clusters'. This problem is a special case of the 'supervised regionalization' or the 'p-regions problem'. The model's functionality is demonstrated on a test landscape and a number of extensions and implementation issues are discussed.
Subject Applied Mathematics not elsewhere classified
Keyword(s) Clustering
Forest fire
Optimization
p-Regions
Prescribed fire
DOI - identifier 10.1007/s11750-015-0383-y
Copyright notice © Sociedad de Estadística e Investigación Operativa 2015
ISSN 1134-5764
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
Altmetric details:
Access Statistics: 127 Abstract Views  -  Detailed Statistics
Created: Thu, 16 Jun 2016, 10:48:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us