Research on Emergency Aid Decision-Making Model for Environmental Emergency Based on Case-Based Reasoning

2014 ◽  
Vol 675-677 ◽  
pp. 206-212
Author(s):  
Ying Ju Zhang
Keyword(s):  
Decision Making ◽  
Nearest Neighbor ◽  
Layer Structure ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Universal Method ◽  
Emergency Decision ◽  
Aid Decision ◽  
Decision Making Model ◽  
Case Based

The paper applied method of case-based reasoning to environmental emergency aid decision-making, and provided a decision-making method based on experience of historical cases to emergency decision-makers. Firstly, a universal method for describing and organizing environmental emergency cases based on three-tier architecture was proposed based on feature analysis of environmental emergency cases. Then a kind of two-layer structure similarity algorithm was designed based on attribute features of environmental emergency cases, which can effectively avoid the defect of traditional Nearest-Neighbor Algorithm. Finally, a CBR prototype system of emergency aid decision-making model was developed and an example of environmental mass incident case was used to testify the practicability of the model. The case example show that the emergency aid decision-making model for environmental emergency based on CBR is applicable in real work.

scholarly journals A Dynamic Emergency Decision-Making Method Based on Group Decision Making with Uncertainty Information

2020 ◽  
Vol 11 (5) ◽  
pp. 667-679
Author(s):  
Jing Zheng ◽  
Yingming Wang ◽  
Kai Zhang ◽  
Juan Liang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Gas Explosion ◽  
Historical Case ◽  
Emergency Decision Making ◽  
Emergency Decision ◽  
Case Based

Abstract In emergency decision making (EDM), it is necessary to generate an effective alternative quickly. Case-based reasoning (CBR) has been applied to EDM; however, choosing the most suitable case from a set of similar cases after case retrieval remains challenging. This study proposes a dynamic method based on case retrieval and group decision making (GDM), called dynamic case-based reasoning group decision making (CBRGDM), for emergency alternative generation. In the proposed method, first, similar historical cases are identified through case similarity measurement. Then, evaluation information provided by group decision makers for similar cases is aggregated based on regret theory, and comprehensive perceived utilities for the similar cases are obtained. Finally, the most suitable historical case is obtained from the case similarities and the comprehensive perceived utilities for similar historical cases. The method is then applied to an example of a gas explosion in a coal company in China. The results show that the proposed method is feasible and effective in EDM. The advantages of the proposed method are verified based on comparisons with existing methods. In particular, dynamic CBRGDM can adjust the emergency alternative according to changing emergencies. The results of application of dynamic CBRGDM to a gas explosion and comparison with existing methods verify its feasibility and practicability.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

Research on the Auxiliary Emergency Decision-Making System of Urban Mass Transit on the Basis of Case-Based Reasoning

2013 ◽  
Vol 361-363 ◽  
pp. 2127-2133
Author(s):  
Hong Fei Yu ◽  
Yong Qin ◽  
Zi Yang Wang ◽  
Zhong Xin Zhao ◽  
Jun Li
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Mass Transit ◽  
System Structure ◽  
Case Based Reasoning ◽  
Urban Mass Transit ◽  
Emergency Decision Making ◽  
Function Module ◽  
Emergency Decision ◽  
Case Based

To meet the requirement of networked urban mass transit, the characteristics of urban mass transit emergency decision-making was analyzed, draw lessons from the thought of case-based reasoning technology, the method which combine CBR technology with expert knowledge was proposed. On the basis of which, the emergency decision-making support system was designed, analyzed the system structure and function module, discussed the key technologies involved in the system, focused on the representation of retrieval technology and case contingency plans and case. Provides support for the modernization of urban mass transit emergency platform.

Case Representation and Organization Studies on Case-Based Reasoning Maintenance Resources Requirement Analysis

2013 ◽  
Vol 389 ◽  
pp. 698-702
Author(s):  
Xiao Chen ◽  
Ling Chen ◽  
Wo Ye Liu ◽  
Fei Han
Keyword(s):  
Prototype System ◽  
Case Based Reasoning ◽  
Requirement Analysis ◽  
Organization Studies ◽  
Case Representation ◽  
Relation Database ◽  
Analysis Process ◽  
System Perfection ◽  
Analysis System ◽  
Case Based

To improve the efficiency of planning maintenance resources requirement, the artificial intelligent (AI) technology, especially Case-Based Reasoning (CBR) is applied into maintenance resources requirement analysis process, the process is introduced, and the critical techniques of which, such as case representation and organization etc, are discussed in detail, according to the case characteristics, analyzed the cases main ingredient, cases representation and organization which is based on Relation Database and Object Oriented are detailed discussed, the development of case-based maintenance resources requirement analysis prototype system proved the validity of the technique, formed the foundation for the case-based maintenance resources requirement analysis system perfection.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

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