ArticlePloS one2023
GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
- Retraction · 2024-10-14Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we investigated the application of Geographic Information Systems (GIS) for groundwater quality assessment through the integration of statistical models with soil and topographical data. Our primary objectives were to identify soil parameters and topographical attributes contributing to groundwater quality assessment and to evaluate the potential of geostatistics and GIS for spatial analysis of groundwater resources. Groundwater samples were collected from 43 agricultural wells, and surface soil layer samples (0-20 cm) were obtained near each well. We measured groundwater quality parameters and relevant soil properties. Our approach involved the utilization of multiple linear regression (MLR) and principal component regression (PCR), combined with topographical terrain attributes and soil data, for modeling groundwater electrical conductivity (GEC). Our findings revealed significant correlations between GEC and soil electrical conductivity (EC) (r = 0.89) as well as soil carbonate (CaCO3) (r = 0.68). Among the ten topographical attributes considered, the terrain wetness index (TWI) exerted the highest influence on GEC (r = 0.57), followed by the slope (r = -0.47). Further analysis demonstrated that the MLR model outperformed the PCR model in both the development and calibration datasets, with an achieved R2value of 0.89 and a root mean square error (RMSE)of 150 μScm-1 for MLR, compared to an R2 of 0.85 and an RMSE of 170 μScm-1 for PCR when coupled with soil and attribute data for GEC prediction. The resulting GEC map generated from the MLR model displayed spatial variations, ranging from 605 μScm-1 in the northern region to 1275 μScm-1 in the central part of the study site. In conclusion, our study demonstrated the effectiveness of combining statistical modeling with geostatistics and GIS for groundwater quality assessment, providing valuable insights for resource management and environmental planning.
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