Abstract
This research uses multivariate statistics to develop the unique dependent variables as inputs for geostatistical analysis in groundwater modeling. The chief aim of the study is to understand if simulation methods are sufficiently accurate to use for groundwater in different regions with various databases. First, factor analysis produced two factors presenting 81.38% and 13.92% of variability for Factor 1 and Factor 2, respectively. Then, the geostatistical estimation based on the ordinary kriging technique was applied to map the factors and prepared the interpolation, estimation variance, and kriging efficiency maps. Kriging is a smoothed interpolation; therefore, the maps based on the sequential Gaussian simulation, and turning band algorithm, were prepared to solve the kriging smoothing in GS+ software and MATLAB software, respectively. Eventually, histograms and variogram of reproductions, obtained from different realizations, were compiled by sequential Gaussian simulation and turning band simulation methods. Results showed that simulation approaches revealed less smoothed synthetic data. The sequential Gaussian simulation is proposed to map the attributes in small scales (large area). Also, the turning band simulation is recommended to apply in large-scale studies. Furthermore, this research proves the ability of the turning band algorithm as an innovative technique in evaluating and modeling the groundwater, which has not been observed so far in other studies in a wide range. This algorithm is proven to improve the quality of estimations and produce maps more accurately.
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Acknowledgements
Special gratitude goes out to Iran Water Resources Management Company for collecting, analyzing, and providing the data. Also, we appreciate the anonymous reviewers for professional reviewing that improves the scientific quality of the paper. Herby, we are grateful to the editorial board of the Journal of Modeling Earth Systems and Environment for their consideration.
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We confirm that the manuscript has been prepared by all authors. Study concept and design: MJ. Acquisition of data: AFM. Analysis and interpretation of data: SK, MJ and AK. Drafting of the manuscript: SK. Critical revision of the manuscript for important intellectual content: SK and AK. Statistical analysis: SK and MJ. Administrative, technical, and material support: MJ. Study supervision: HK.
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Karami, S., Jalali, M., Karami, A. et al. Evaluating and modeling the groundwater in Hamedan plain aquifer, Iran, using the linear geostatistical estimation, sequential Gaussian simulation, and turning band simulation approaches. Model. Earth Syst. Environ. 8, 3555–3576 (2022). https://doi.org/10.1007/s40808-021-01295-1
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DOI: https://doi.org/10.1007/s40808-021-01295-1