scholarly journals Evaluation Model of the Campus Emergency Management Capacity Based on Principal Component Projection Method

2012 ◽  
Vol 37 ◽  
pp. 299-303 ◽  
Author(s):  
Song Xi-bo ◽  
He Hai-yan ◽  
Zhu Xiang-yu
Keyword(s):  
Emergency Management ◽  
Projection Method ◽  
Evaluation Model ◽  
Principal Component ◽  
Management Capacity ◽  
Campus Emergency Management

scholarly journals Study on Evaluation Model of Emergency Rescue Capability of Chemical Accidents Based on PCA-BP

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jianghong Liu ◽  
Junfeng Wu ◽  
Weisi Liu
Keyword(s):  
Neural Network ◽  
Emergency Management ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Model ◽  
Principal Component ◽  
Assessment Model ◽  
Chemical Accidents ◽  
Emergency Rescue ◽  
Model Based

The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities. In this study, an emergency rescue capability assessment model based on the PCA-BP neural network is proposed. Firstly, the construction status of 11 emergency rescue teams for chemical accidents in Shanghai is analyzed, and an index system for evaluating the capabilities of emergency rescue teams for chemicals is established. Secondly, the principal component analysis (PCA) is used to perform dimension reduction and indicators’ weight acquisition on the original index system to achieve an effective evaluation of the capabilities of 11 rescue teams. Finally, the indicators after dimensionality reduction are used as the input neurons of the backpropagation (BP) neural network, the characteristic data of eight rescue teams are used as the training set, and the comprehensive scores of three rescue teams are used for verifying the generalization ability of the evaluation model. The result shows that the proposed evaluation model based on the PCA-BP neural network can effectively evaluate the rescue capability of the emergency rescue teams for chemical accidents and provide a new idea for emergency rescue capability assessment.

Metro Emergency Management Capacity Assessment Research Based on AHP-FCE Model

2013 ◽  
Vol 405-408 ◽  
pp. 1314-1317
Author(s):  
Qing Xue Shi ◽  
Li Xin Li ◽  
Shi Long Jia ◽  
Yi Bo Zhang ◽  
Shuang Zhang
Keyword(s):  
Emergency Management ◽  
Management System ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Analytic Hierarchy ◽  
Operation System ◽  
Emergency Management System ◽  
Capacity Evaluation ◽  
Management Capacity

Based on the characteristics of the Metro Operation System and the types of accidents, linked with Metro Operation Emergency Management System, this thesis develops a System of Metro Emergency Management Capacity Evaluation Index. Whats more, with the application of Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), it also sets up the Metro Emergency Management Capacity Evaluation Model AHP - FCE Comprehensive Evaluation Model. Finally, the model comprehensively evaluates the ability of the domestic Metro Operation Emergency Management System. By analyzing the evaluation results, the author finds out the vulnerable spots of the system and puts forward his own opinions for improvement.

Spatial clustering analysis of green economy based on knowledge graph

2021 ◽  
pp. 1-10
Author(s):  
Shiyuan Zhou ◽  
Xiaoqin Yang ◽  
Qianli Chang
Keyword(s):  
Spatial Data ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Principal Component ◽  
Green Economy ◽  
Membership Degree ◽  
Two Dimensional ◽  
Dimensional Graph ◽  
Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

Evaluation of emergency management capability of urban community based on entropy weight and multi-layer fuzzy

2021 ◽  
pp. 1-9
Author(s):  
Wang Yuansheng ◽  
Zhang Ying ◽  
Guo Xinyao
Keyword(s):  
Emergency Management ◽  
Index System ◽  
Comprehensive Evaluation ◽  
Urban Community ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Entropy Weight ◽  
Management Capability ◽  
The World ◽  
Comprehensive Evaluation Model

At present, various public emergencies occur frequently around the world, and the world has entered the stage of a “high-risk society”. Urban community as the carrier of all kinds of public emergencies, its role has become increasingly prominent and become the focus of the current research, but in the urban community emergency management capacity evaluation system, the related studies are still less. To more effectively identify the key internal and external factors that affect the emergency management ability of the urban community, and evaluate scientifically and effectively, on the basis of the existing studies, the evaluation index system of community emergency management capability was established according to the emergency management cycle theory. In view of the complexity and fuzziness of the emergency management capability of the urban community, a multi-layer fuzzy comprehensive evaluation model based on entropy weight was established, and the emergency management capability evaluation of community was carried out under the background of the public emergency event of COVID-19 epidemic. The results show that the evaluation results and improvement suggestions of the multi-layer fuzzy comprehensive evaluation model based on entropy weight are consistent with the actual situation, indicating that the index system and the selected method are reasonable and effective. This study provides a new decision-making idea and method for the evaluation of urban community emergency management capability, and has high application value.

Comparison on Dam Risk Consequences Comprehensive Evaluation Model

2013 ◽  
Vol 785-786 ◽  
pp. 1465-1468
Author(s):  
Wei Wei Sun ◽  
Lei Li ◽  
Hao Yao Zheng ◽  
Xue Ying Zhao
Keyword(s):  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Correlation Method ◽  
Principal Component ◽  
Principal Component Method ◽  
Weighted Sum Method ◽  
Comprehensive Evaluation Model ◽  
Gray Correlation ◽  
Mathematics Method ◽  
Dam Risk

Many evaluation factors are involved in dam risk consequences comprehensive evaluation, which exist plenty of uncertainty and correlation between each other. Combined the five methods, such as linear weighted sum method, fuzzy mathematics method, matter element method, gray correlation method and principal component method with dam risk consequences comprehensive evaluation, establishing five dam risk comprehensive evaluation models, applying to the level evaluation of Changlong Reservoir, Xialan Reservoir, Shibikeng Reservoir, Longshan Reservoir, Lingtan Reservoir successfully.

scholarly journals Data-driven integration evaluation from the perspective of Adaboost and its application in WeChat public number ranking

2020 ◽  
Vol 309 ◽  
pp. 02017
Author(s):  
Yicheng Gong ◽  
Juan Zhao ◽  
Dongyang Zhang
Keyword(s):  
Factor Analysis ◽  
Adaptive Learning ◽  
Evaluation Model ◽  
Complex Structure ◽  
Principal Component ◽  
Data Driven ◽  
Analysis Method ◽  
Topsis Method ◽  
Evaluation Models ◽  
Weak Evaluation

The traditional comprehensive evaluation is difficult to model when dealing with large data with large parameters and complex structure, and it cannot adapt to the update of data. In order to improve this situation, this paper draws on the Adaptive Learning Adaboost perspective in statistical learning to develop a data-driven integrated evaluation model that updates the weight of sample weights and weak evaluation models with data. Three specific weak evaluation models were selected: data-driven Topsis method, principal component analysis method and factor analysis method. Taking the ranking of WeChat public account as an example, the results show that the accuracy of the integrated evaluation model is 88.57%, which is 17.14%, 31.43% and 28.57% higher than the data-driven Topsis method, principal component method and factor analysis method.

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