Studying the use of forest management decision support systems: an initial synthesis of lessons learned from case studies compiled using a semantic wiki

2013 ◽  
Vol 29 (sup1) ◽  
pp. 44-55 ◽  
Author(s):  
Sean N. Gordon ◽  
Antonio Floris ◽  
Luc Boerboom ◽  
Tomas Lämås ◽  
Ljusk Ola Eriksson ◽  
...  
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support Systems ◽  
Case Studies ◽  
Support Systems ◽  
Lessons Learned ◽  
Management Decision ◽  
Semantic Wiki ◽  
Management Decision Support

scholarly journals Review. A brief overview of forest management decision support systems (FMDSS) listed in the FORSYS wiki

2013 ◽  
Vol 22 (2) ◽  
pp. 263 ◽  
Author(s):  
T. Packalen ◽  
A. Marques ◽  
J. Rasinmäki ◽  
C. Rosset ◽  
F. Mounir ◽  
...  
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Management Decision Support

The forest management decision support system project

1991 ◽  
Vol 67 (6) ◽  
pp. 622-628 ◽  
Author(s):  
Dan Bulger ◽  
Harold Hunt
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Forest Management ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Decision Makers ◽  
Management Decision ◽  
Management Decision Support

The focus of a decision support system is much different from Management Information Systems (MIS) and data-based "decision support systems". Decision support systems, as defined by the authors, focus on decisions and decision makers, and on information. Technology is treated as a tool and data as the raw material. In many traditional systems the focus is on the technology, and the data is the "information", while decision makers are, to some extent, externalized.The purpose of the Forest Management Decision Support System (FMDSS) project is to develop a set of software tools for creating forest management decision support systems. This set of tools will be used to implement a prototype forest management decision support system for the Plonski forest, near Kirkland Lake, Ontario.There are three critical ingredients in building the FMDSS, these are: (1) knowledge of the decision making process, (2) knowledge of the forest, and (3) the functionality of underlying support technology. The growing maturity of the underlying technology provides a tremendous opportunity to develop decision support tools. However, a significant obstacle to building FMDSS has been the diffuse nature of knowledge about forest management decision making processes, and about the forest ecosystem itself. Often this knowledge is spread widely among foresters, technicians, policy makers, and scientists, or is in a form that is not easily amenable to the decision support process. This has created a heavy burden on the project team to gather and collate the knowledge so that it could be incorporated into the function and design of the system. It will be difficult to gauge the success of this exercise until users obtain the software and begin to experiment with its use.

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.

Management Decision Support Systems.

1980 ◽  
Vol 25 (2) ◽  
pp. 376 ◽  
Author(s):  
Jack William Jones ◽  
Andrew M. McCosh ◽  
Michael S. Scott Morton ◽  
Peter G. Keen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Management Decision Support
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