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IJSTR >> Volume 4 - Issue 8, August 2015 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Multivariate Analysis Of Ground Water Characteristics Of Geological Formations Of Enugu State Of Nigeria

[Full Text]

 

AUTHOR(S)

Orakwe, LC, Chukwuma, EC

 

KEYWORDS

Keywords: Borehole Characteristics, Multivariate Analysis, Cluster Analysis, Principal Factor Analysis, Geological Formations, Enugu state.

 

ABSTRACT

Abstract: The chemometric data mining techniques using principal factor analysis (PFA), and hierarchical cluster analysis (CA), was employed to evaluate, and to examine the borehole characteristics of geological formations of Enugu State of Nigeria to determine the latent structure of the borehole characteristics and to classify 9 borehole parameters from 49 locations into borehole groups of similar characteristics. PFA extracted three factors which accounted for a large proportion of the variation in the data (77.305% of the variance). Out of nine parameters examined, the first PFA had the highest number of variables loading on a single factor where four borehole parameters (borehole depth, borehole casing, static water level and dynamic water level) loaded on it with positive coefficient as the most significant parameters responsible for variation in borehole characteristics in the study. The CA employed in this study to identified three clusters. The first cluster delineated stations that characterise Awgu sandstone geological formation, while the second cluster delineated Agbani sandstone geological formation. The third cluster delineated Ajali sandstone formation. The CA grouping of the borehole parameters showed similar trend with PFA hence validating the efficiency of chemometric data mining techniques in grouping of variations in the borehole characteristics in the geological zone of the study area.

 

REFERENCES

[1] Secretariat of the Convention on Biological Diversity. (2010) Drinking Water, Biodiversity and Development: A Good Practice Guide. Montreal, 41 + iii pages.

[2] Donkor, S.M.K. and Wolde, Y.E (2008) Integrated water resources management in Africa: issues and options. United Nations Economic Commission for Africa.

[3] MacDonald, A.M., Davies, J. and O’Dachartaigh, B.E (2001) Simple Methods for Assessing Groundwater Resources in Low Permeability Area of Africa.British Geological Survey Commissioned Report CR/01/168N, UK; BGS Keyworth.

[4] Landau, S., and Everitt, B. S., (2004) A handbook on statistical analyses using SPSS. Chapman & Hall/CRC press LLC.

[5] Wang, Y., Peng, W., Yujun, B., Zaixing, T., Jingwen, Li., Xue, S., Laura, F. M., Bai-Lian L., (2013) Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region, China. Journal of Hydro-environment Research 7;30-40.

[6] Vialle, C., Sablayrolles, C., Lovera, M., Jacob, S., Huau, M. C., Vignoles, M., (2011) Monitoring of water quality from roof runoff: Interpretation using multivariate analysis. Water research 45; 3765-3775.

[7] Shrestha, S., Kazama, F., (2007). Assessment of surface water quality using multivariate statistical techniques, A case study of the Fuji river basin, Japan. Environmental Modeling and Software 22 (4), 464–475.

[8] Mutihac, L., Mutihac, R., (2008). Mining in chemometrics. Analytica Chimica Acta 612(1), 1–18.

[9] Forina, M., Armanino, C., Raggio, V., (2002) Clustering with dendrograms on interpretation variables. Analytica Chimica Acta 454 (1), 13–19.

[10] Liu, C.W., Lin, K.H., Kuo, Y.M., (2003) Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. Science in the Total Environment 313, 77-89.

[11] Templ, M., Filzmoser, P., Reimann, C., (2008) Cluster analysis applied to regional geochemical data, problems and possibilities. Applied Geochemistry 23 (8),2198–2213.