Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

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ID: 261627
2020
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Abstract
Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales.
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nhu2020watermapping Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Viet-Ha Nhu;Omid Rahmati;Fatemeh Falah;Saeed Shojaei;Nadhir Al-Ansari;Himan Shahabi;Ataollah Shirzadi;Krzysztof Górski;Hoang Nguyen;Baharin Bin Ahmad;Nhu, Viet-Ha;Rahmati, Omid;Falah, Fatemeh;Shojaei, Saeed;Al-Ansari, Nadhir;Shahabi, Himan;Shirzadi, Ataollah;Górski, Krzysztof;Nguyen, Hoang;Ahmad, Baharin Bin;
Journal water
Year 2020
DOI
10.3390/w12040985
URL
https://dx.doi.org/10.3390/w12040985
Keywords
Logistic regression gis ensemble model karst springs certainty factor

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