ESSDD

Earth System Science Data Discussions

ESSDD

Earth Syst. Sci. Data Discuss.

1866-3591

Göttingen, Germany

10.5194/essd-2016-29

Hydrochemical assessment of Semarang area using multivariate statistics: A sample based dataset

Irawan

Dasapta Erwin

https://orcid.org/0000-0002-1526-0863

¹ Putranto

Thomas Triadi

Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung – 40132, Indonesia

Faculty of Engineering, Universitas Diponegoro, Jalan Prof. H. Soedarto, SH, Tembalang, Kota Semarang – 50275, Indonesia

28 07 2016

2016 1 11

2016

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://essd.copernicus.org/preprints/essd-2016-29/

The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2016-29/essd-2016-29.pdf

The following paper describes in brief the data set related to our project "Hydrochemical assessment of Semarang Groundwater Quality". All of 58 samples were taken in 1992, 1993, 2003, 2006, and 2007 using well point data from several reports from Ministry of Energy and Min- eral Resources and independent consultants. We provided 20 parameters in each samples (sample id, coord X, coord Y, well depth, water level, water elevation, TDS, pH, EC, K, Ca, Na, Mg, Cl, SO4, HCO3, year, ion balance, screen location, and chemical facies). The chemical composi- tion were tested in the Water Quality Laboratory, Universitas Diponegoro using mas spectrofotometer method. <br><br> The statistical treatment for the dataset (available on Zenodo <a href="http://dx.doi.org/10.5281/zenodo.57293" target="_blank">doi:10.5281/zenodo.57293</a>) were described as follows: (1) data preparation in to csv file format, load it in to R environment; (2) data treatment, including: correlation matrix, cluster analysis using kmeans and hierarchical cluster analysis, and principal component analysis. For anal- ysis and visualizations, We used the following R packages: <tt>ggplot2</tt>, <tt>dplyr</tt>, <tt>factomineR</tt>, <tt>factoExtra</tt>, <tt>cluster</tt>, <tt>ggcorrplot</tt>, and <tt>ape</tt>.