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GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

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Abstract

This study evaluated and compared groundwater spring potential maps produced with two different models—namely multivariate adaptive regression spline (MARS) and random forest (RF)—using geographic information system (GIS). In total, 234 spring locations were identified in the Boujnord, North Khorasan, Iran and a GIS spring inventory map was prepared. Of these, 176 (70 %) locations were employed to produce spring potential maps (training), while the remaining 58 (30 %) cases were used to validate the model. The explanatory variables used to predict spring location were altitude, slope aspect, slope degree, slope length, topographic wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance to rivers, drainage density, distance to faults, and fault density. Furthermore, the spatial relationships between spring occurrence and explanatory variables were performed using a Certainty Factor (CF) model. For validation, area under a receiver operating characteristics (ROC) curves (AUC) was used. The validation results showed that the AUC for calibration is almost identical (0.79) in both models, while for prediction, the MARS model (73.26 %) performed better than RF (70.98 %) model. These results indicate that the MARS and RF models are good estimators of groundwater spring potential in the study area. These groundwater spring potential maps can be applied to groundwater management and groundwater resource exploration.

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Acknowledgments

The authors would like to thank Dr. Michael Fienen at the USGS Wisconsin Water Science Center for revising of language of manuscript. Also, we gratefully acknowledge of Editor-in-Chief Prof. James W. LaMoreaux and the two anonymous reviewers for their helpful comments on the previous version of the manuscript.

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Authors and Affiliations

  1. Young Researchers and Elite Club, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran

    Mohsen Zabihi

  2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  3. Department of Environment Management Engineering, College of Natural Resources, Yazd University, Yazd, Iran

    Zohre Sadat Pourtaghi

  4. Planning and Management Organization, Bojnourd, North-Khorasan Province, Iran

    Morteza Behzadfar

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  1. Mohsen Zabihi
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  2. Hamid Reza Pourghasemi
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  3. Zohre Sadat Pourtaghi
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Correspondence to Hamid Reza Pourghasemi.

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Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z.S. et al. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci 75, 665 (2016). https://doi.org/10.1007/s12665-016-5424-9

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