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

Viet‐Ha NhuFaculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamOmid RahmatiSoil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education center, AREEO, Sanandaj 36311-66169, IranFatemeh FalahDepartment of Watershed Management, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan 68151-44316, IranSaeed ShojaeiYoung Researchers and Elite Club, Zahedan branch, Islamic Azad University, Zahedan 98167-43545, IranNadhir Al‐AnsariDepartment of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, SwedenHiman ShahabiBoard Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, IranAtaollah ShirzadiDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranK. GórskiFaculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Chrobrego 45 Street, 26-200 Radom, PolandHoang NguyenInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamBaharin Bin AhmadDepartment of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia

2020en

ABI

Annotatsiya

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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DOI: 10.3390/w12040985

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