| Issue |
MATEC Web Conf.
Volume 192, 2018
The 4th International Conference on Engineering, Applied Sciences and Technology (ICEAST 2018) “Exploring Innovative Solutions for Smart Society”
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 4 | |
| Section | Track 2: Mechanical, Mechatronics and Civil Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201819202007 | |
| Published online | 14 August 2018 | |
Cluster and regression analysis for predicting salinity in groundwater
1
Master degree student, Environmental and energy engineering for sustainability, Department of Civil Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand, 10520.
2
Assoc.Prof.Dr, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand, 10520.
3
Assoc.Prof.Dr, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand, 10520.
*
Corresponding author : jack_ozaka@hotmail.com
Groundwater salinity is a major problem particularly in the northeastern region of Thailand. Saline groundwater can cause widespread saline soil problem resulting in reducing agricultural productivity as in the Lower Nam Kam River Basin. In order to better manage the salinity problem, it is important to be able to predict the groundwater salinity. The objective of this research was to create a cluster-regression model for predicting the groundwater salinity. The indicator of groundwater salinity in this study was electrical conductivity because it was simple to measure in field. Ninety-eight parameters were measured including precipitation, surface water levels, groundwater levels and electrical conductivity. In this study, the highest groundwater salinity at 3 wells was predicted using the combined cluster and multiple linear regression analysis. Cross correlation and cluster analysis were applied in order to reduce the number of parameters to effectively predict the quality. After the parameter selection, multiple linear regression was applied and the modeling results obtained were R2 of 0.888, 0.918, and 0.692, respectively. This linear regression model technique can be applied elsewhere in the similar situation.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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