scholarly journals Risk Assessment of Debris Flow Disaster Based on Principal Component Analysis

2021 ◽  
Vol 781 (2) ◽  
pp. 022111
Author(s):  
Dejie Yu ◽  
Lansong Lv
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Debris Flow ◽  
Principal Component ◽  
Component Analysis ◽  
Debris Flow Disaster

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.

Risk Assessment of Investment Projects Based on Principal Component Analysis

2011 ◽  
Vol 467-469 ◽  
pp. 2048-2053
Author(s):  
Yong Chen Li ◽  
Ling Zhang ◽  
Jun Shi He
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Evaluation Method ◽  
Principal Component ◽  
Component Analysis ◽  
System Evaluation ◽  
Investment Risk ◽  
Analysis Method ◽  
Investment Projects ◽  
Evaluation Problem

Investment risk assessment is a very complex system evaluation problem. According to the evaluation results, investors can determine whether to invest in the project and take measures to avoiding risk.Choosing a evaluation method reasonablely is the key step.In this paper, the writer uses principal component analysis to evaluate investment projects and the principal component analysis method also can be exerted more proficiently.

Sign in / Sign up

Export Citation Format

Share Document