scholarly journals Effective Use of Ensemble Numerical Weather Predictions in Taiwan by Means of a SOM-Based Cluster Analysis Technique

Water ◽  
2017 ◽  
Vol 9 (11) ◽  
pp. 836 ◽  
Author(s):  
Ming-Chang Wu ◽  
Jing-Shan Hong ◽  
Ling-Feng Hsiao ◽  
Li-Huan Hsu ◽  
Chieh-Ju Wang
Keyword(s):  
Cluster Analysis ◽  
Numerical Weather ◽  
Analysis Technique ◽  
Cluster Analysis Technique ◽  
Effective Use

Ten-Year Results of a Ponderosa Pine Progeny Test in the Black Hills

1986 ◽  
Vol 1 (3) ◽  
pp. 79-83 ◽  
Author(s):  
Wayne D. Shepperd ◽  
Sue E. McElderry
Keyword(s):  
Cluster Analysis ◽  
Ponderosa Pine ◽  
Black Hills ◽  
Progeny Test ◽  
Poor Survival ◽  
Analysis Technique ◽  
Survival And Growth ◽  
Cluster Analysis Technique ◽  
Growth Of Seedlings

Abstract Ten-year survival and growth of seedlings from 77 parent trees from throughout the Black Hills were compared, using a cluster-analysis technique. Five clusters were identified that account for most of the variability in survival and growth of the open-pollinated families. One cluster, containing 6 families, exhibited exceptional survival and growth. Another, containing 12 families, exhibited poor survival and growth. The performance of families in these two groups appears to be related to location and elevation of parent trees. West. J. Appl. For. 1:79-83, July 1986.

scholarly journals Comparing the utilities consumptions in Dubai per category and community: MANOVA and cluster analysis approaches

2021 ◽  
Vol 958 (1) ◽  
pp. 012022
Author(s):  
A Saihi ◽  
A Alzaatreh
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Water Desalination ◽  
Analysis Technique ◽  
Open Government Data ◽  
Government Data ◽  
Increasing Demand ◽  
And Cluster Analysis ◽  
Cluster Analysis Technique ◽  
Desalination Plants

Abstract UAE is marked by the increasing demand for water and electricity due to demographic, environmental and economic factors, coupled with the dependence on water desalination process, which is costly, consumes a lot of energy and is non-environmentally friendly. Like most of the authorities in UAE, Dubai Electricity and Water Authority is facing the challenges of balancing supply with demand and responding to consumer requirements, from one side, and addressing the continuously increasing consumption and slowing it down from another side. Therefore, policy makers can benefit from statistical data analysis in order to make informed decisions. This study aims to equip decision makers with useful tools and analysis to address some of their short- and long-term objectives related to production and consumption. The current study focused on three main objectives: (i) analysing the production of the desalination plants in Dubai, (ii) comparing the consumptions of water and electricity based on the four categories residential, commercial, industrial and others, and (iii) segmenting the various communities in Dubai depending on their consumption behavior. The data used for this study is collected from the open government data and SAS Programming is adopted for data analysis. The results of the analysis revealed that the desalinated water production follows an upward trend, yet still not in line with the consumption growth. Furthermore, there are significant differences between the four categories for both water and electricity consumptions. The highest levels of consumptions are associated with the residential and commercial categories. Finally, the cluster analysis technique revealed fifteen clusters of communities depending on the consumption levels.

scholarly journals Copper ore type definition from Sossego Mine using X-ray diffraction and cluster analysis technique

2012 ◽  
Vol 65 (4) ◽  
pp. 561-566 ◽  
Author(s):  
Viviane Kotani Shimizu ◽  
Henrique Kahn ◽  
Juliana L. Antoniassi ◽  
Carina Ulsen
Keyword(s):  
Cluster Analysis ◽  
Mineralogical Composition ◽  
X Ray Diffraction ◽  
Copper Ore ◽  
X Ray ◽  
Type Definition ◽  
Analysis Technique ◽  
And Cluster Analysis ◽  
Cluster Analysis Technique

This paper presents the classification of 110 copper ore samples from Sossego Mine, based on X-ray diffraction and cluster analysis. The comparison based on the position and the intensity of the diffracted peaks allowed the distinction of seven ore types, whose differences refer to the proportion of major minerals: quartz, feldspar, actinolite, iron oxides, mica and chlorite. There was a strong correlation between the grouping and the location of the samples in Sequeirinho and Sossego orebodies. This relationship is due to different types and intensities of hydrothermal alteration prevailing in each body, which reflect the mineralogical composition and thus the X-ray diffractograms of samples.

scholarly journals 3D Reservoir Rock Type Model Based on Cluster Analysis Technique for Rumaila Formation in the Ahdeb Oil Field, Central Ira

2021 ◽  
Vol 11 (2) ◽  
pp. 49-73
Author(s):  
Buraq Adnan Hussein Al-Baldawi
Keyword(s):  
Cluster Analysis ◽  
Rock Type ◽  
Oil Field ◽  
Reservoir Rock ◽  
Type Model ◽  
Shale Volume ◽  
Rock Types ◽  
Analysis Technique ◽  
Cluster Analysis Technique

This study presents is a classification of reservoir properties (porosity and shale volume) into rock types for carbonate Rumaila reservoir in Central Iraq (Ahdeb Field). The Cluster analysis method is used to identify rock types and to recognize well log clusters of similar characteristics. For most subsurface research, the determination of the rock type (lithofacies and petrofacies) is not adequate enough because of a lack of cores and cuttings. An interactive petrophysics software program was used to get the results of a cluster analysis technique, in order to determine the rock typing (log facies) in Rumaila formation units in the Ahdeb oil field. Initially, petrophysical parameters such as porosity, shale volume and quantity of various reservoir minerals were determined using the probabilistic evaluation process. In the second stage, the multi-resolution graphic clustering method was employed to separate the sequential electrofacies which resulted in the identification of four electrofacies with different geological reservoir properties. The vertical variations of the rock type for Rumaila formation are based on four log facial groups. These log groups are categorized according to porosity and shale volume of formation based on responses to well logs after division of Rumaila formation into four units (Ru-1, Ru-2, Ru-3, and Ru-4).A 3D rock type model for Rumaila Formation was performed using Petrel software in order to illustrate the horizontal distribution of rock type along the Ahdeb field and showing the best characterized of reservoir rock type in any unit of Rumaila Formation. Cluster analysis technique classified porosity and shale volume, which were calculated for Rumaila Formation using well logs, into four similar characteristics rock types: rocktype-1, rock type-2, rock type-3 and rock type-4. A 3D Petrel model of rock type shows that rock type-2 has better reservoir quality than other rock types in Rumaila Formation which is characterized by high porosity and low shale volume. The model clarifies the distribution of rock type-2 in the Ahdeb field at units Ru-1 and Ru-3 of Rumaila Formation

Attribute Recognition of Buried Pipes Based on Multi-Trace Phase Features Using K-means Clustering for GPR Data Interpretation

2021 ◽  
Vol 26 (2) ◽  
pp. 117-132
Author(s):  
Deshan Feng ◽  
Xun Wang ◽  
Hua Zhang ◽  
Jun Yang ◽  
Zhongming Yuan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Phase Difference ◽  
Laboratory Data ◽  
Object Identification ◽  
Connected Components ◽  
Pipe Material ◽  
Difference Analysis ◽  
Analysis Technique ◽  
Attribute Recognition ◽  
Cluster Analysis Technique

Accurate location and depth determination of underground pipes, especially the attribute recognition, are of great importance yet remake a challenging issue in municipal environments. Single-trace phase difference analysis remains a bottleneck due to its inherent and strong randomness in object identification. This paper developed a multi-trace phase difference analysis framework for ground-penetrating radar (GPR) data based on K-means cluster analysis technique and the theory of region of interest (ROI), which could serve as a new criterion for successful pipe attribute recognition. After improving signal-to-noise ratio of GPR data by using the preprocessing techniques, the connected components algorithms (CCA) based on image segmentation and morphological operation is performed to delineate the ROI. The K-means cluster analysis technique is further employed to efficiently extract the multi-trace phase statistical features for comprehensively evaluating the attributes of ROI. We verify this proposed framework by simulated GPR signals, laboratory data and field datasets. Results demonstrate that the proposed method can not only facilitate the attribute recognition of pipes, but also reduce the interpretation ambiguity of the pipe material even in the field site environment. Specifically, if the phase difference of pipe turns out to be even multiples of π, the target can be automatically identified as metallic-category pipes, whereas odd multiples of π, point to non-metallic-category pipes with a lower permittivity than that of the background. This criterion presents promising applicability in subsurface pipeline identification and attributes recognition, especially in constructing a more appropriate initial model of GPR full waveform inversion for survey in pipes.

Clustering Tourists Based on Reason for Destination Choice

2019 ◽  
pp. 289-303
Author(s):  
Ulas Akkucuk ◽  
Serhat Ata
Keyword(s):  
Cluster Analysis ◽  
Optimal Number ◽  
Destination Choice ◽  
Multiple Choice Questions ◽  
Number Of Clusters ◽  
Factors Affecting ◽  
Tourism Marketing ◽  
Analysis Technique ◽  
Cluster Analysis Technique ◽  
Shed Light

This study aims to explore the factors affecting destination choice for tourism travelers. For this purpose, a survey conducted in the İzmir province has been used to collect data as to what factors drive travelers to choose the İzmir destination. The survey contains demographic questions in addition to questions concerning the reasons for visiting İzmir. 10 multiple choice questions are used in the demographic and descriptive profile. Previous literature has been consulted in order to form 16 questions for the intent to visit İzmir. The questions on the reasons for visiting Izmir have been scaled as (1) does not influence my decision and (5) influence(s) my decision very much. Finally using cluster analysis technique, the visitors are separated into five clusters. A K-means clustering technique is applied by varying k from 2 to 10. The interpretations of the clusters are provided and shed light on the main intentions for the travelers. The cluster interpretations could be used in the area of tourism marketing. The chapter highlights the area of choosing the optimal number of clusters.

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