| Issue |
MATEC Web Conf.
Volume 270, 2019
The 2nd Conference for Civil Engineering Research Networks (ConCERN-2 2018)
|
|
|---|---|---|
| Article Number | 04017 | |
| Number of page(s) | 5 | |
| Section | Water Resources Engineering and Management | |
| DOI | https://doi.org/10.1051/matecconf/201927004017 | |
| Published online | 22 February 2019 | |
Association rules and regression linear model of the groundwater population by the evaluation of uranium
1
Department of Management, Faculy of Economics, Universitas Malahayati, Bandarlampung, Indonesia
2
Department of Environmental Engineering, Faculty of Engineering, Universitas Malahayati, Bandarlampung, Indonesia
* Corresponding author: deiingofthelukman@gmail.com
The uranium available more on groundwater samples of certain types on the total alkalinity were relatively the same. But, the content of the uranium was higher in the samples. The multiple linear regression for pH as a dependent variable showed that the pH negatively correlated to the uranium, but the uranium was not significant for the linear regression model. The data of groundwater population from the samples of 127 with 12 variables of measurement of the Energy Department of the United States of America resulted in those association rules and linear regression models. The data has five factors of Producing horizon namely Ogallala Formation (TPO), Dockum Formation (TRD), Quartermaster Group (POQ), Whitehorse and Cloud Chief Group (PGWC), El Reno Group and Blaine Formation (PGEB). The step-wise linear regression for each of the five producing horizon codes was fitted to the data. Then, the regression models for each variable of producing horizon were obtained if pH was the dependent variable. If the Uranium was a dependent variable, then the regression models obtained were four only, with the model for PGEB was not able to be made. When pH as a dependent variable, it was depended upon Boron, Total alkalinity, and Bicarbonate.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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