Applying Principal Component Analysis and Group-Decision Analytical Hierarchy Process for the Fuzzy Risk Assessment of Construction Project

2012 ◽  
Vol 6 (1) ◽  
pp. 779-782 ◽  
Author(s):  
Huawang Shi
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Analytical Hierarchy Process ◽  
Group Decision ◽  
Construction Project ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Risk Assessment ◽  
Hierarchy Process

scholarly journals Measuring local competitiveness: comparing and integrating two methods PCA and AHP

2021 ◽  
Author(s):  
Katarzyna A. Kurek ◽  
Wim Heijman ◽  
Johan van Ophem ◽  
Stanisław Gędek ◽  
Jacek Strojny
Keyword(s):  
Social Sciences ◽  
Principal Component Analysis ◽  
Analytical Hierarchy Process ◽  
Mixed Method ◽  
Principal Component ◽  
Component Analysis ◽  
Mixed Modelling ◽  
Hierarchy Process ◽  
Comparable Model ◽  
Non Parametric

AbstractThis article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination.

scholarly journals The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021 ◽  
Vol 13 (24) ◽  
pp. 13859
Author(s):  
Shu Wu
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Spatial Distribution ◽  
Forest Fire ◽  
Forest Fires ◽  
Inner Mongolia ◽  
Information Diffusion ◽  
Principal Component ◽  
Component Analysis ◽  
Expected Number

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

scholarly journals Risk assessment of equipment at gas storage depots based on clustering and principal component analysis

2021 ◽  
Vol 859 (1) ◽  
pp. 012113
Author(s):  
Jing Zhang ◽  
Jinlei Yuan ◽  
Renyuan Wang ◽  
Mingxing Luo ◽  
Yang Chen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Storage

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

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