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Geostatistical Analysis of Groundwater Data
Reshma H S1, P N Chandramouli2

1Reshma H S, M.tech in Hydraulics, Department of Civil Engineering, National Institute of Engineering, Mysore, India.
2P. N. Chandramouli, Professor, Department of Civil Engineering, National Institute of Engineering, Mysore, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2734-2741 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9758109119/2019©BEIESP | DOI: 10.35940/ijeat.A9758.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Water resources are stressed because of the country’s increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.
Keywords: Agglomerative hierarchical clustering, K-means cluster, Statistical method.