Principal component analysis and hierarchical cluster analyses of arsenic groundwater geochemistry in the Hetao basin, Inner Mongolia

Geochemistry ◽  
2015 ◽  
Vol 75 (2) ◽  
pp. 197-205 ◽  
Author(s):  
Yuxiao Jiang ◽  
Huaming Guo ◽  
Yongfeng Jia ◽  
Yongsheng Cao ◽  
Chao Hu
Keyword(s):  
Principal Component Analysis ◽  
Inner Mongolia ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Cluster Analyses ◽  
Groundwater Geochemistry ◽  
Hierarchical Cluster Analyses

scholarly journals Morphmetric analysis of apis cerana populations in Huangshan, China

2013 ◽  
Vol 57 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Linsheng Yu ◽  
Fang Liu ◽  
Sisi Huang ◽  
Shoudong Bi ◽  
Chao Zong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Honey Bees ◽  
Apis Cerana ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Morphometric Characters ◽  
Morphometric Methods ◽  
Wilks Lambda ◽  
Hierarchical Cluster Analyses

Abstract Honey bees (Apis cerana Fabricius) were collected from 195 colonies at seven different localities spanning the main beekeeping areas in Huangshan. Morphometric methods were used to measure seven standard morphometric characters, and these bees were compared to samples from the Henan, Shandong, and Yunnan provinces. Principal component analysis of the total Huangshan database yielded two clusters: bees from Jinxian and Jixixian, and those from other localities. Within the latter cluster, discriminant and hierarchical cluster analyses revealed overlapping regional sub-clusters: bees from Huangshanqu, Qimenxian, Huizhouqu, and Shexian, and those from Yixian. Significant differences between the means of the three clusters were demonstrated using Wilks’ lambda statistic. Morphocluster separation was related to altitude differences. Moreover, we noted some regions with high intercolonial variance, suggesting introgression among these defined honeybee populations.

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 Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

Sign in / Sign up

Export Citation Format

Share Document