Towards an integrated framework for forest fire control

Dimopoulou, Maria; Giannikos, Ioannis

doi:10.1016/s0377-2217(03)00038-9

Cited by 62 publications

(27 citation statements)

References 17 publications

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“…The basic models-subsystems which comprise the structural elements for managing forest fires, and most DSSs use, are the following (Bonazountas et al 2007;Dimopoulou and Giannikos 2004;European Commission 2012a;Giovando et al 2013;Glasa 2009;Gumusay and Sahin 2009;Kalabokidis et al 2012;Keramitsoglou et al 2004;Lee et al 2002Lee et al , 2010Noonan-Wright et al 2011;Wybo 1998):…”

Section: Structural Elements Of Dsssmentioning

confidence: 99%

“…All the above, including the avoidance of overlap between agencies saves resources and improves the effectiveness of fighting similar fire incidents (National Interagency Fire Center 2013). Dimopoulou and Giannikos (2004) used an integrated system for forest fire control. This consisted of a geographic information system (GIS) module, a mathematical programing (MP) module, and a simulation model.…”

Section: Dsss Based On Database Management Systems and Mathematical/ementioning

confidence: 99%

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Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

et al. 2017

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Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems (DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/ economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

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Section: Structural Elements Of Dsssmentioning

confidence: 99%

Section: Dsss Based On Database Management Systems and Mathematical/ementioning

confidence: 99%

Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

et al. 2017

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“…Dimopoulou and Giannikos (2004) have for example developed a framework for forest fire control and Ng and Raff (2007) have been discussing the firefighter problem. The system developed by Dimopoulou and Giannikos (2004) consists of a combination of simulation based technique with optimization models and a geographical information system (GIS) module which can be utilized as a flexible tool to assist planners in the design of a complete strategy for forest fire management. The mathematical programming module is used to determine the number of needed resources, where they must be placed, or how they should be rearranged in order to control the fire effectively and to achieve the maximum possible coverage.…”

Section: Operational Planning 35mentioning

confidence: 99%

“…Real time information is also needed in the system for forest fire control described by Dimopoulou and Giannikos (2004). This study considers decision making at an operational level where online information is important to plan the firefighting and the firefighters' reallocations effectively.…”

Section: Dynamic Planning 37mentioning

confidence: 99%

“…Other information that can be necessary for solving various planning problems, especially in the short term, can be the time of the day, traffic congestion, weather conditions, expected operation time, the accidents' behavior, the firefighters' and the fire vehicles' location etc. (Dimopoulou and Giannikos, 2004;Zaki and Cheng, 1997;Schmid and Doerner, 2010).…”

Section: Input Data Requirements 38mentioning

confidence: 99%

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Optimization Based Decision Support Tools for Fire and Rescue Resource Planning

Ulander¹

2015

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When accidents occur, it is essential that fire and rescue service respond quickly and efficiently to the accident site to reduce suffering and save lives and property. Planning and coordinating of fire and rescue resources is therefore important in order to maintain a safe society. Firefighters in Sweden have by tradition worked in teams of five, and they have been allocated to fire stations strategically located in populated areas. However, this working approach has recently started to change and the fire and rescue services have started to deploy smaller groups of firefighters. These smaller units can be, for example, strategically located near high-risk areas or roads, or used for preventive work. The complexity of the resource planning process thus increases since the decision makers have to keep track of a large number of small units spread over the area. The new way of working has resulted in an increased need of support tools that can help the fire and rescue services in decisions regarding the resource management.In this thesis, optimization based decision support tools are developed in order to to support the fire and rescue service so they can efficiently manage, coordinate and dispatch fire and rescue resources with respect to the present demand for service.To find appropriate strategic and tactical locations for different types of response units, an optimization model that minimizes the response time to expected accidents is developed. The model considers both the response time for the first responding unit as well as the response time for the last responding unit. Furthermore, the model is flexible enough to incorporate any type of accidents or resources. The results show that the model can be used to produce practical support for various types of location decisions.To support the more complex operational planning, three optimization based decision support tools are developed. The tools can help the fire and rescue service to always maintain an adequate level of preparedness for handling accidents. To evaluate the preparedness, a quantitative measure has been defined and serves as the basis for the tools. The first tool can help illustrate the preparedness, the second tool to select appropriate vehicles and firefighters to dispatch to accidents, and the third tool to suggest how resources can be relocated to maintain an adequate preparedness for new accidents.The developed decision support tools are evaluated through tests and experiments with fire and rescue services in Sweden. Results from the experiments indicate that the three tools can support the fire and rescue service in operational decisions and in maintaining an adequate preparedness for handling accidents, but also that the planning time seems to increase when using the support tools. However, the participants' experiences of the tools were in general positive, and they thought the tools were useful and that the tools could support their daily work. The perception of preparedness, which usually varies quite a bit among individual...

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