Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, China, using principal component analysis and an improved fuzzy C-means algorithm

2015 ◽  
Vol 75 (3) ◽  
pp. 909-922 ◽  
Author(s):  
Mingyuan Shi ◽  
Jianping Chen ◽  
Yang Song ◽  
Wen Zhang ◽  
Shengyuan Song ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Debris Flow ◽  
Inner Mongolia ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy C Means ◽  
Debris Flow Susceptibility ◽  
Fuzzy C Means Algorithm

The Evaluation of Comprehensive Transportation System in Inner Mongolia Based on Principal Component Analysis

2014 ◽  
Vol 505-506 ◽  
pp. 782-786
Author(s):  
Chun Mei Zhang ◽  
Zhan Xin Ma ◽  
Lu Lu Zhai ◽  
Xin Yu Cui ◽  
Xiao Biao Zhao
Keyword(s):  
Principal Component Analysis ◽  
Inner Mongolia ◽  
Rapid Development ◽  
Principal Component ◽  
Component Analysis ◽  
Transportation System ◽  
Autonomous Region ◽  
Economic Output ◽  
Transportation Development ◽  
Inner Mongolia Autonomous Region

Based on the relevant data of comprehensive transportation system in Inner Mongolia Autonomous Region from 1990 to 2011, the transport equipment, transport mileage, transport capacity, and the transport share of the total economic output in four aspects are studied. Then we select 13 indicators to build the evaluation of comprehensive transportation system in Inner Mongolia Autonomous Region. Using SPSS17.0 software to perform the principal component analysis could get the evaluation of the development of comprehensive transportation system in Inner Mongolia, which has maintained rapid development in the past 22 years, especially after 2003, higher than previous years. It is in accordance with the current transportation development of Inner Mongolia Autonomous Region, next we verify the feasibility of the Principal Component Analysis (PCA) on transportation problem. The method also has theoretical significance of research on relevant aspects of other areas.

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 Classifying Hybrids of Energy Cane for Production of Bioethanol and Cogeneration of Biomass-Based Electricity by Principal Component Analysis-Linked Fuzzy C-Means Clustering Algorithm

2019 ◽  
Vol 11 (14) ◽  
pp. 246 ◽  
Author(s):  
B. R. A. Moreira ◽  
R. S. Viana ◽  
L. A. M. Lisboa ◽  
P. R. M. Lopes ◽  
P. A. M. Figueiredo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Cluster ◽  
Alcoholic Beverages ◽  
Type I ◽  
Energy Cane ◽  
Fuzzy C Means

The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast-fermented alcoholic beverages, soft drinks, silage and high-quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and could serve as energy supply pathways to produce bioethanol, while the hybrids from fuzzy cluster III are type II energy cane (lower sucrose to fiber ratio), denoting potential as higher fiber yield biomass sources to feed cogeneration of heat and bioelectricity in high temperature and pressure furnace-boiler system.

Sign in / Sign up

Export Citation Format

Share Document