Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support

2011 ◽  
Vol 262 (12) ◽  
pp. 2133-2141 ◽  
Author(s):  
Zachary A. Holden ◽  
W. Matt Jolly
Keyword(s):  
Decision Support ◽  
Complex Terrain ◽  
Fire Management ◽  
Wildland Fire ◽  
Fire Danger ◽  
Management Decision ◽  
Fuel Moisture ◽  
Wildland Fire Management ◽  
Management Decision Support

Information systems in support of wildland fire management decision making in Canada

2002 ◽  
Vol 37 (1-3) ◽  
pp. 185-198 ◽  
Author(s):  
B.S Lee ◽  
M.E Alexander ◽  
B.C Hawkes ◽  
T.J Lynham ◽  
B.J Stocks ◽  
...  
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Fire Management ◽  
Wildland Fire ◽  
Management Decision ◽  
Management Decision Making ◽  
Wildland Fire Management

Built structure identification in wildland fire decision support

2011 ◽  
Vol 20 (1) ◽  
pp. 78 ◽  
Author(s):  
David E. Calkin ◽  
Jon D. Rieck ◽  
Kevin D. Hyde ◽  
Jeffrey D. Kaiden
Keyword(s):  
At Risk ◽  
Decision Support ◽  
Fire Management ◽  
Wildland Fire ◽  
Management Strategies ◽  
Fire Spread ◽  
The United States ◽  
Structure Identification ◽  
Incident Management ◽  
Wildland Fire Management

Recent ex-urban development within the wildland interface has significantly increased the complexity and associated cost of federal wildland fire management in the United States. Rapid identification of built structures relative to probable fire spread can help to reduce that complexity and improve the performance of incident management teams. Approximate structure locations can be mapped as specific-point building cluster features using cadastral data records. This study assesses the accuracy and precision of building clusters relative to GPS structure locations and compares these results with area mapping of housing density using census-based products. We demonstrate that building clusters are reasonably accurate and precise approximations of structure locations and provide superior strategic information for wildland fire decision support compared with area density techniques. Real-time delivery of structure locations and other values-at-risk mapped relative to probable fire spread through the Wildland Fire Decision Support System Rapid Assessment of Values at Risk procedure supports development of wildland fire management strategies.

Exploring Adoption of the Wildland Fire Decision Support System: End User Perspectives

2020 ◽  
Vol 118 (2) ◽  
pp. 154-171 ◽  
Author(s):  
Peter Noble ◽  
Travis B Paveglio
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fire Management ◽  
Wildland Fire ◽  
Management Decision ◽  
Us Forest Service ◽  
User Perspectives ◽  
Depth Interviews

Abstract Abstract The increasing complexity of wildland fire management highlights the importance of sound decision making. Numerous fire management decision support systems (FMDSS) are designed to enhance science and technology delivery or assist fire managers with decision-making tasks. However, few scientific efforts have explored the adoption and use of FMDSS by fire managers. This research couples existing decision support system research and in-depth interviews with US Forest Service fire managers to explore perspectives surrounding the Wildland Fire Decision Support System (WFDSS). Results indicate that fire managers appreciate many WFDSS components but view it primarily as a means to document fire management decisions. They describe on-the-ground actions that can be disconnected with decisions developed in WFDSS, which they attribute to the timeliness of WFDSS outputs, the complexity of the WFDSS design, and how it was introduced to managers. We conclude by discussing how FMDSS development could address concerns raised by managers.

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Sign in / Sign up

Export Citation Format

Share Document