Mathematical Models, Values of Parameters, and the Sensitivity Analysis of Management-Decision Rules

1957 ◽  
Vol 21 (4) ◽  
pp. 419 ◽  
Author(s):  
Richard B. Maffei
Keyword(s):  
Sensitivity Analysis ◽  
Mathematical Models ◽  
Decision Rules ◽  
Management Decision

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.

scholarly journals The effects of flooding and weather conditions on leptospirosis transmission in Thailand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sudarat Chadsuthi ◽  
Karine Chalvet-Monfray ◽  
Anuwat Wiratsudakul ◽  
Charin Modchang
Keyword(s):  
Sensitivity Analysis ◽  
Public Education ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Weather Conditions ◽  
Transmission Dynamics ◽  
Multiplication Rate ◽  
High Degree ◽  
Transmission Models

AbstractThe epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.

scholarly journals Using catch rate data for simple cost-effective quota setting in the Australian spanner crab (Ranina ranina) fishery

2010 ◽  
Vol 67 (8) ◽  
pp. 1538-1552 ◽  
Author(s):  
Michael F. O'Neill ◽  
Alexander B. Campbell ◽  
Ian W. Brown ◽  
Ron Johnstone
Keyword(s):  
Decision Rules ◽  
Cost Effective ◽  
Catch Rate ◽  
Management Decision ◽  
Rate Data ◽  
Total Allowable Catch ◽  
Management Procedure ◽  
Annual Total ◽  
New Methodologies ◽  
Catch Rates

Abstract O'Neill, M. F., Campbell, A. B., Brown, I. W., and Johnstone, R. 2010. Using catch rate data for simple cost-effective quota setting in the Australian spanner crab (Ranina ranina) fishery. – ICES Journal of Marine Science, 67: 1538–1552. For many fisheries, there is a need to develop appropriate indicators, methodologies, and rules for sustainably harvesting marine resources. Complexities of scientific and financial factors often prevent addressing these, but new methodologies offer significant improvements on current and historical approaches. The Australian spanner crab fishery is used to demonstrate this. Between 1999 and 2006, an empirical management procedure using linear regression of fishery catch rates was used to set the annual total allowable catch (quota). A 6-year increasing trend in catch rates revealed shortcomings in the methodology, with a 68% increase in quota calculated for the 2007 fishing year. This large quota increase was prevented by management decision rules. A revised empirical management procedure was developed subsequently, and it achieved a better balance between responsiveness and stability. Simulations identified precautionary harvest and catch rate baselines to set quotas that ensured sustainable crab biomass and favourable performance for management and industry. The management procedure was simple to follow, cost-effective, robust to strong trends and changes in catch rates, and adaptable for use in many fisheries. Application of such “tried-and-tested” empirical systems will allow improved management of both data-limited and data-rich fisheries.

scholarly journals Substituting model-based indicators in Harvest Control Rules by observations using fuzzy logic methodology

2017 ◽  
Vol 75 (3) ◽  
pp. 977-987
Author(s):  
Arne Eide
Keyword(s):  
Fuzzy Logic ◽  
Mathematical Models ◽  
Stock Assessment ◽  
Decision Rules ◽  
Initial Information ◽  
Stock Size ◽  
Control Rules ◽  
Stock Biomass ◽  
Size Evaluation ◽  
Harvest Control Rules

Abstract Harvest Control Rules are predefined heuristic decision rules to provide quota advices for managed fisheries. Frequently statistical methods and biological assumptions expressed in mathematical models, are used to provide the Harvest Control Rules with initial information (indicators values). The aim of this article is to investigate a possible way forward of replacing these inputs by quantities of measurable observations, e.g. catch-at-age statistics. The article presents a method by which recruitment indexes and stock biomass indicators are obtained by non-parametric use of annual catch-at-age records, without filtering the raw data (observations) through mathematical models. Two related methods, applied on three empirical cases, are provided: First, showing that recruitment strengths of the Northeast Arctic cod, haddock, and saithe stocks, obtained by fuzzy logic methodology, are satisfactory captures by the use of catch-at-age data. Second, stock size indicators are estimated for the three species by the same catch-at-age data. The second task turns out to be more challenging than the first, but also in the case of stock size evaluation, the suggested procedure provides reasonable results when compared to standard stock assessment methods.

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