Abstract
Through the use of several variables, including rainfall, distance to faults, slope, geology, distance to rivers, land use, and soils, hotspots and cold spots of groundwater potential (GWP) zones have been identified. Ordinary Least Squares (OLS) regression modeling was applied to the best results to determine the degree to which the factors and GWP zone hotspots were related. To identify the data points that have a negative silhouette coefficient, “Cluster Outlier Analysis” was used. Except for the slope gradient, all the parameters demonstrated positive correlation coefficients. In our study, the VIF values for land use range from 1.01021 to 1.40733. (geology). The chosen factors/variables were able to account for 85% of the hotspots. To examine the behavior of the OLS regression’s standardized residuals, the spatial autocorrelation tool (Global Morans’s I) was used. This study demonstrated the effectiveness of Optimized Hot Spot Analysis in conjunction with OLS regression as a potent tool for comprehending the influence of factors on GWP zones that can be applied at a regional and continental scale.
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IA, MZ, and MAD drafted the methodology, interpreted the results. AF drafted the literature review. AHT, MD, MB, and MN reviewed the paper with necessary corrections.
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Ahmad, I., Zelenakova, M., Fenta, A. et al. Configuring the groundwater potential zone spatially using optimized hotspot analysis. Environ Earth Sci 82, 467 (2023). https://doi.org/10.1007/s12665-023-11160-2
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DOI: https://doi.org/10.1007/s12665-023-11160-2