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Extracting of prospective groundwater potential zones using remote sensing data, GIS, and a probabilistic approach in Bojnourd basin, NE of Iran

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Abstract

Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.

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Authors and Affiliations

  1. Department of Applied Geology, Faculty of Earth Sciences, Kharazmi University, Tehran, Iran

    Majid Altafi Dadgar, Majid Dashti & Rasool Porhemmat

  2. Department of Remote Sensing and GIS, Kharazmi University (Teacher Training University), Tehran, Iran

    Parviz Zeaieanfirouzabadi

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  1. Majid Altafi Dadgar
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  2. Parviz Zeaieanfirouzabadi
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  3. Majid Dashti
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  4. Rasool Porhemmat
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Correspondence to Majid Altafi Dadgar.

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Altafi Dadgar, M., Zeaieanfirouzabadi, P., Dashti, M. et al. Extracting of prospective groundwater potential zones using remote sensing data, GIS, and a probabilistic approach in Bojnourd basin, NE of Iran. Arab J Geosci 10, 114 (2017). https://doi.org/10.1007/s12517-017-2910-7

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