Abstract
This study investigates the effectiveness of combining the Group Method of Data Handling (GMDH) models and Kriging to reduce errors in groundwater salinity estimation. The study uses two datasets of electrical conductivity (EC) values collected from 109 observation wells in an agricultural region of Iran. The methodology comprises a two-step process. Initially, GMDH models were trained and validated to estimate groundwater EC values at any desired location based on the observed EC values from surrounding wells. Subsequently, these models were employed to expand the EC datasets, followed by using the Kriging interpolation approach to investigate the spatial variability of groundwater EC across the study area. During the cross-validation phase, the GMDH models achieved robust results. For the first dataset, the model maintained an average R-squared value of 0.81, with a corresponding root mean square error (RMSE) of 0.61 dS/m. Similarly, for the second dataset, the model performed well with an R-squared value of 0.84 and RMSE of 0.72 dS/m. The findings also indicated that employing GMDH to extend the original datasets improves the results provided by Kriging, particularly when the original datasets are increased by 40%. On average, for the first dataset, employing the integrated approach decreased the RMSE of Kriging by 18.34%, while for the second dataset, the decrease in RMSE was 19.21%. Overall, the findings suggest that expanding datasets can enhance the accuracy of estimating water quality variables, which has important implications for environmental monitoring and management.
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Data availability
The data used in this study are openly available for download from the following URL: https://data.wrm.ir/
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Acknowledgements
We extend our heartfelt thanks to the editorial team for their meticulous review and the anonymous reviewers for their invaluable insights that greatly improved our manuscript. We also appreciate the generous data support from the Qazvin Regional Water Authority, which was essential for this research.
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The research was supported by the University of Guilan.
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The study conception and design were contributed to by all authors. Hamed Amini performed the material preparation and data collection. Hamed Amini, Afshin Ashrafzadeh, and Mohammadreza Khaledian conducted the data analysis and interpreted the results. The initial draft of the manuscript was written by Afshin Ashrafzadeh, and all authors provided feedback and revisions on earlier versions of the manuscript. All authors have read and approved the final manuscript.
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Amini, H., Ashrafzadeh, A. & Khaledian, M. Enhancing groundwater salinity estimation through integrated GMDH and geostatistical techniques to minimize Kriging interpolation error. Earth Sci Inform 17, 283–297 (2024). https://doi.org/10.1007/s12145-023-01157-7
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DOI: https://doi.org/10.1007/s12145-023-01157-7