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GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data

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  • Yuwen Chen

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.

Suggested Citation

  • Yuwen Chen, 2023. "GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-18, November.
    • Handle: RePEc:plo:pone00:0292680
    DOI: 10.1371/journal.pone.0292680
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    References listed on IDEAS

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    1. Lazhar Belkhiri & Tahoora Narany, 2015. "Using Multivariate Statistical Analysis, Geostatistical Techniques and Structural Equation Modeling to Identify Spatial Variability of Groundwater Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2073-2089, April.
    2. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
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