Multivariate analysis of ground water characteristics of Ajali sandstone formation: A case study of Udi and Nsukka LGAs of Enugu State of Nigeria

Orakwe, Chukwuemeka L.; Chukwuma, E. C.

doi:10.1016/j.jafrearsci.2017.02.011

Cited by 5 publications

(9 citation statements)

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“…Then, the second linear combination (uncorrelated with the first) which accounts for the next biggest amount of variance (after removing the one attributed to the first component) is extracted. The process continues until all variance has been accounted for by extracting all components regarded as significant in the dataset (Orakwe and Chukwuma, 2017). The number of principal components retained and interpreted in the current study was based on the following criteria (Mertler and Vannatta, 2005), (i) Kaiser's rule on eigenvalue (ii) the scree test (iii) assessment of model fit, and (iv) components accounting for at least 70% total variability.…”

Section: Principal Component Analysismentioning

confidence: 99%

“…The use of traditional approaches to study the chemistry of groundwater in Benin Formation aquifer have been reported (Erah et al, 2002;Ukpebor and Unuigbe, 2003;Imasuen and Omorogieva, 2013;Idugboe et al, 2014;Akpoborie et al, 2015). There is need for the application of multivariate statistical approaches which have been validated in other places (Olobaniyi and Owoyemi, 2006;Balogun and Akoteyon, 2012;Zhang et al, 2012;Yang et al, 2015;Armanuos et al, 2016;Zhang et al, 2016;Orakwe and Chukwuma, 2017) for hydrochemical characterization of Benin Formation in Benin-city. A combination of hierarchical cluster analysis (HCA) and principal component analysis (PCA) were therefore employed in grouping groundwater samples on the basis of similarities in multidimensional space as well as reduction of bulk hydrochemical data into components which explain likely underlying structures existing among hydrochemical clusters.…”

Section: Introductionmentioning

confidence: 99%

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Hydrochemical Characterization of Benin Formation in Benin-city and Environs, Nigeria using Multivariate Analyses

2019

JSTR

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Multivariate statistics was applied to some hydrochemical data from Benin Formation in Benin-city metropolis, Southern Nigeria with a view to characterizing and determining the main structures underlying groundwater chemistry in the area. Laboratory chemical analysis followed standard laboratory analytical procedures stipulated by American Society for Testing and Materials (ASTM). Hydrochemical data were standardized to z-scores and screened for outliers, normality and linearity prior to statistical analysis at significant level of 5%. Q-mode Hierarchical Cluster Analysis (HCA) grouped the groundwater samples into distinct clusters to represent different hydrochemical facies. R-mode Principal Component analysis (PCA) reduced bulk hydrochemical data to discrete principal components indicating possible dominant processes responsible for groundwater chemistry. Results of PCA revealed six principal components (PCs) which together explain 86.84 % of the total variance in the dataset. The results of PCA implied that Ca, total hardness, Mg, total dissolved solids, chloride, electrical conductivity and Na are the most significant parameters controlling groundwater chemistry in the area. The extracted components also indicated that atmospheric controls and silicate mineral weathering processes are the main factors responsible for variation in groundwater chemical variations in the area. Results of HCA which identified six significant clusters indicated high degree of spatial, statistical and chemical coherence, and validated grouping of groundwater into chemical clusters in the study area. Results from this study showed PCA and HCA are useful tools for analyzing dominant processes responsible for variation in groundwater chemistry in the study area.

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Section: Principal Component Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hydrochemical Characterization of Benin Formation in Benin-city and Environs, Nigeria using Multivariate Analyses

2019

JSTR

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“…Principal Component Analysis (PCA) of Statistical Packages for Social Sciences (SPSS) program version 20.0 was used to combine these factors affecting water supply into a few underlying dimensions. PCA is statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of lineally uncorrelated variables called principal components (Anyadike, 2009;Vialle et al, 2011;Orakwe and Chukwuma, 2015). PCA combines large number of indicators into fewer, more analogous groups, each group defining the underlying dimension in the contributing variables forming the group (Anyadike, 2009).…”

Section: Analysis Of Data Collectedmentioning

confidence: 99%

“…The indicators have been computed as the sums of squares of deviations divided by N-1 (where N is the valid number of cases). Significant principal factors (PCs) with eigenvalues greater than unity (i.e., PCs explaining more than the variance of one indicator) were extracted (Orakwe and Chukwuma, 2015). Orthogonal rotation using variance maximisation varimax was used to maximise the variance of the squared component loadings for each component, repartitioning the loadings towards higher components, thus improving interpretation (Anyadike, 2009).…”

Section: Analysis Of Data Collectedmentioning

confidence: 99%

Factors Responsible For Rural Residential Water Supply Shortage In Southeastern Nigeria

Obeta

Nwankwo

2015

Journal of Environmental Geography

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Efficient water supply is very crucial to sustenance of socio-economic growth, poverty attenuation, and food security. In most rural areas of developing countries including Nigeria water supplies are not commensurate with demand leading to a shortfall in water use and many people suffer from this scenario. This research investigated the factors responsible for rural residential water supply shortage in Southeastern Nigeria. Data were collected through the use of four mixed but complimentary methods namely questionnaire survey, interviews, focus group discussions and secondary data sources. The data generated were analyzed through the use of descriptive and inferential statistical tools. Principal Component Analysis was employed to combine the variables accounting for water supply shortage into a few underlying dimensions. The results indicated that physical environment and inadequate water supply infrastructure; socio-economic and geographical location; management and socio-cultural problems are responsible for water supply shortage in the area. Residential water supply can be sustainable in the area by regulating the influence of these factors impacting on water supply as well as lessen the implications of water deficiency. The research concludes that the process of water supply development should be stepwise in accordance with the participatory and managerial capacity of communities.

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“…Along with the development of machine learning, multivariate analytical technology has been applied in some different areas of the geochemical research, the fourth paradigm for the research is becoming a more and more powerful tool to find a solution among the mass data. The multivariate analysis has been used to study the water characteristics [29], source [30,31], groundwater pathway [32][33][34], etc. By using the method of multivariate technology, it is possible to disclose the leaching mechanism from the view of trace element occurrence and leaching behavior.…”

Section: Introductionmentioning

confidence: 99%

Leaching Mechanisms of Trace Elements from Coal and Host Rock Using Method of Data Mining

Shan¹

2022

Artificial Intelligence

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Coal and host rock, including the gangue dump, are important sources of toxic elements, which have high-contaminating potential to surface and groundwater. Surface water in the coal mine area and groundwater in the active or abandoned coal mines have been observed to be polluted by trace elements, such as arsenic, mercury, lead, selenium, cadmium. It is helpful to control pollution caused by the trace elements by understanding the leaching behavior and mechanism. The leaching and migration of the trace elements are controlled mainly by two factors, trace elements’ occurrence and the surrounding environment. The traditional method to investigate elements’ occurrence and leaching mechanism is based on the geochemical method. In this research, the data mining method was applied to find the relationship and patterns, which is concealed in the data matrix. From the geochemical point of view, the patterns mean the occurrence and leaching mechanism of trace elements from coal and host rock. An unsupervised machine learning method, principal component analysis was applied to reduce dimensions of data matrix of solid and liquid samples, and then, the re-calculated data were clustered to find its co-existing pattern using the method of Gaussian mixture model.

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Multivariate analysis of ground water characteristics of Ajali sandstone formation: A case study of Udi and Nsukka LGAs of Enugu State of Nigeria

Cited by 5 publications

References 15 publications

Hydrochemical Characterization of Benin Formation in Benin-city and Environs, Nigeria using Multivariate Analyses

Hydrochemical Characterization of Benin Formation in Benin-city and Environs, Nigeria using Multivariate Analyses

Factors Responsible For Rural Residential Water Supply Shortage In Southeastern Nigeria

Leaching Mechanisms of Trace Elements from Coal and Host Rock Using Method of Data Mining

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