Abstract
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.
Highlights
Groundwater is an important natural resource for ecosystem, organism and human living
Water quality indexes prediction was applied by multiple linear regression modeling [2,7]
The statistical significance of models is 95%, the regression models of Y1 Y2 Y3 can be described by regression equations with R2 of 88.8%, 91.8%, 69.2%, respectively, indicated the regression models appropriated for predicting the dependent variables, which were electrical conductivity at 3 wells
Summary
Groundwater is an important natural resource for ecosystem, organism and human living. The groundwater salinity problem was important to study at the risk areas. The important issue of this research is groundwater salinity due to rapid population, rapid industrialization growth and the use of enormous chemicals in agriculture because of poor management. Electrical conductivity was a useful indicator of saline in this research because it was easy to measure in fieldwork. A few numbers of literatures were used regression equation for groundwater quality prediction data in different areas. Ground water samples from different area have been analyzed for correlation between electrical conductivity and parameters. An attempt has been made to develop linear regression for predicting the concentration of water quality constituents having significant correlation coefficients with electrical conductivity. Water quality indexes prediction was applied by multiple linear regression modeling [2,7]. Cluster analysis was used to assess the water quality and it useful to manage, control pollution and protect water quality [12]
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