scholarly journals Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis

2017 ◽  
Vol 140 (3) ◽  
pp. 1131-1157 ◽  
Author(s):  
Marko Kallio ◽  
Joseph H. A. Guillaume ◽  
Matti Kummu ◽  
Kirsi Virrantaus
Keyword(s):  
Principal Component Analysis ◽  
Spatial Variation ◽  
Principal Component ◽  
Component Analysis ◽  
Poverty Index ◽  
Weighted Principal Component ◽  
Water Poverty Index

scholarly journals The use of principal component analysis for the construction of the Water Poverty Index

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Larynne Dantas de Senna ◽  
Adelena Gonçalves Maia ◽  
Joana Darc Freire de Medeiros
Keyword(s):  
Principal Component Analysis ◽  
Poverty Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Poverty Index ◽  
Multiple Dimensions ◽  
Water Poverty Index ◽  
Clear Identification ◽  
Natural Characteristics ◽  
Insight Into

ABSTRACT In relation to water resources, indexes can be created to express the multiple dimensions involved with it to aid the planning and management of basins. In this regard, the Water Poverty Index is globally used, but one of its criticisms includes the subjectivity associated with how the sub-indexes are weighted. Therefore, in this study, we applied principal component analysis (PCA) to determine the sub-indexes’ weight: resource, access, capacity, use, and environment of the Seridó river basin. This new index with PCA presents an average range with broader values compared to methodologies without, allowing clear identification of the disparities among the cities and the possibility to better prioritize investments concerning water poverty reduction. Our results show that this approach makes it possible to qualitatively identify geographical locations that have greater water poverty compared to others. Additionally, with this approach, it can be determined whether water poverty is caused due to natural characteristics or deficits in water infrastructure investment, providing insight into social fragilities as well. Overall, the presented hierarchical tool in this study has a high value to improve the planning of water resource uses.

Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

2012 ◽  
Vol 622-623 ◽  
pp. 45-50 ◽  
Author(s):  
Joydeep Roy ◽  
Bishop D. Barma ◽  
J. Deb Barma ◽  
S.C. Saha
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Arc Welding ◽  
Principal Component ◽  
Traverse Speed ◽  
Component Analysis ◽  
Submerged Arc Welding ◽  
Bead Geometry ◽  
Weighted Principal Component ◽  
Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

scholarly journals On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

2016 ◽  
Vol 38 (3) ◽  
pp. 1208-1223 ◽  
Author(s):  
Francisco Jesús Martinez-Murcia ◽  
Meng-Chuan Lai ◽  
Juan Manuel Górriz ◽  
Javier Ramírez ◽  
Adam M. H. Young ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Brain Structure ◽  
Site Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Weighted Principal Component ◽  
Structure Heterogeneity ◽  
The Brain
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