Spatial Optimization of Resources Deployment for Forest‐Fire Management

Abstract

The main objective of forest-®re management is to minimize the damage caused by forest ®res. This may be achieved by deploying ®re-®ghting forces in critical locations so as to detect ®res as early as possible and quickly dispatch an initial attack force that can control them while they are small. Empirical studies have identi®ed several factors that affect the development of a forest ®re. In the present study, a methodology is employed to classify regions within a forest area according to these factors. The classi®cation is based on information provided by a Geographic Information System (GIS). This information is then passed on to a spatial optimization model that determines the optimal location of ®re-®ghting resources. A maximal covering model is employed that takes into account the classi®cation of regions by varying coverage in regions of different class. The method has been applied to the area of Parnitha, near Athens.

A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping

“…The health of forest in a given area is a true indicator of the ecological condition prevailing in that area (Saklani 2008). In general, fire is a natural component of many forest ecosystems and cannot be avoided (Dimopoulou & Giannikos 2001). Forest fires cause major damages to environment, human health and property, and endanger life (Rawat 2003).…”

A decision support system for firebase location in a nature conservation area

“…A notable exception is the model of Dimopoulou and Giannikos [17], who employed a maximum coverage model, taking into account the classification of a forest by varying coverage importance in regions of different GIS classes and applied the model to the area of Parnitha, near Athens in Greece. Although Liu et al [40] also employed GIS-related data in their model for emergency response facility location, this was in the urban context of covering transportation routes for hazardous materials in Singapore.…”

Uncertainty and risk in wildland fire management: A review

“…MacLellan and Martell (1996) modeled airtanker deployment as a queuing system with airtankers as servers and fires as customers, and developed an optimization model to identify home-basing strategies to minimize the average annual cost of satisfying daily airtanker deployment demands. Some authors have adopted maximal covering models, which seek to provide coverage to demand areas within a time or distance limit (e.g., Dimopoulou and Giannikos, 2002;Hodgson and Newstead, 1978). Expansions to these models include simulation with scenario analysis to allow for uncertainty in model parameters (e.g., Haight and Fried, 2007;Dimopoulou and Giannikos, 2004).…”