Forest management planning expert system for wildfire damage reduction

Kaloudis, S.; Yialouris, Constantine P.; Lorentzos, Nikos A.; Karteris, M.; Sideridis, Alexander B.

doi:10.1016/j.compag.2009.07.020

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…The following criteria were evaluated: effectiveness, usefulness, understanding of questions and user friendliness, and the outcome were presented as a percentage, from 0 to 100%. Similar evaluations of decision support systems were performed by Mahaman (2002Mahaman ( , 2003, Gonza-lez-Andujar (2006), Kaloudis (2010).…”

Section: Controlmentioning

confidence: 75%

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The Expert System Supporting Decision-Making in the Process of Vegetable Pests Extermination During Vegetation Period

Sojak,

Głowacki,

Tulej

et al. 2023

Agricultural Engineering

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The paper presents a computer system supporting the identification of vegetable pests during the vegetation period and the way in which it works on the example of red beet. The objective was to develop an expert system to facilitate the identification of pests and suggest an appropriate method of controlling them. Filling the database with the knowledge applying to one narrow area of knowledge turns the expert system framework into an expert system in this area of knowledge. The system consists of the expert system and the database in the form of text files, which contain additional explanations. The user of the expert system - “DSS – pest control” needs to answer the following questions: in the first stage the user selects the diagnosed vegetable, in the second stage, the user selects the symptom or symptoms on the above-ground vegetable part, in the third stage, the user selects the symptom or symptoms on the below-ground vegetable part. The designed decision support system (“DSS – pest control”) may be used by individual vegetable growers. It may also serve as an educational program, e.g. for students who want to find out more about the specific areas of knowledge as well as for scientists and researchers.

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Section: Controlmentioning

confidence: 75%

“…Correctness of reasoning rules was equal 100%. Moreover, the system verification was achieved by systematic testing of the functionality of all its components during the implementation phase (Kaloudis et al, 2010). Having finished the implementation of all the rules and other components of the expert system, it was evaluated.…”

Section: Controlmentioning

confidence: 99%

The Expert System Supporting Decision-Making in the Process of Vegetable Pests Extermination During Vegetation Period

Sojak,

Głowacki,

Tulej

et al. 2023

Agricultural Engineering

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“…Developing and using wildfire risk assessment models can aid active and preventative forest management decision-making (González et al, 2006). The literature continues to expand with examples of wildfire risk analyses from around the globe (e.g., Atkinson et al, 2010;Dlamini, 2010;Kaloudis et al, 2010;Li et al, 2009;Zhijun et al, 2009). Planning scales for wildfire management range from incidentspecific to regional/national assessment, and applications include fire prevention, fire detection, deployment and initial attack dispatch, large fire management, and strategic planning and fuel management .…”

Section: Introductionmentioning

confidence: 98%

Uncertainty and risk in wildland fire management: A review

Thompson

Calkin

2011

Journal of Environmental Management

229

138

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“…A wide range of models and decision support systems exist to help support risk-informed wildfire decision making, many of which specifically target one or more of the aforementioned sources of uncertainty [Ager et al, 2014;Chuvieco et al, 2012;Kaloudis et al, 2010;Calkin et al, 2011a;Noonan-Wright et al, 2011;Petrovic and Carlson, 2012;Rodríguez y Silva and González-Cabán, 2010;Salis et al, 2012]. In a prefire planning environment, structured decision processes can systematically and deliberatively address uncertainties with a range of techniques [Warmink et al, 2010;Marcot et al, 2012;Thompson et al, 2013a;Skinner et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and Probability in Wildfire Management Decision Support

Thompson

Calkin

Scott³

et al. 2016

Geophysical Monograph Series

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Wildfire risk assessment is increasingly being adopted to support federal wildfire management decisions in the United States. Existing decision support systems, specifically the Wildland Fire Decision Support System (WFDSS), provide a rich set of probabilistic and risk-based information to support the management of active wildfire incidents. WFDSS offers a wide range of decision-support components, including fire behavior modeling, fire weather information, air quality and smoke management, economics, organization assessment, and risk assessment. Here we focus on WFDSS's provision of probabilistic information and how it can facilitate strategic and tactical decision making. However, the management of active wildfire incidents can be highly complex and subject to multiple uncertainties, only some of which are addressed by WFDSS. We review remaining uncertainties, including identified issues in how fire managers interpret and apply probabilistic information, and conclude with observations and predictions for the future direction of risk-based wildfire decision support.

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Forest management planning expert system for wildfire damage reduction

Cited by 6 publications

References 31 publications

The Expert System Supporting Decision-Making in the Process of Vegetable Pests Extermination During Vegetation Period

The Expert System Supporting Decision-Making in the Process of Vegetable Pests Extermination During Vegetation Period

Uncertainty and risk in wildland fire management: A review

Uncertainty and Probability in Wildfire Management Decision Support

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