Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS Ali Golkarian & Seyed Amir Naghibi & Bahareh Kalantar & Biswajeet Pradhan Received: 20 February 2017 /Accepted: 24 January 2018 # Springer International Publishing AG, part of Springer Nature 2018 Abstract Ever increasing demand for water resources for different purposes makes it essential to have better under- standing and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topo- graphic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algo- rithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculat- ed. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC- ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclu- sion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facil- itate water resource exploitation. Keywords Iran . Modeling . Mapping . R statistical software . Geographic information system Introduction Groundwater is an extremely important water resource, particularly in areas having arid and semi-arid condition such as Iran (Chezgi et al. 2015), and provides a high Environ Monit Assess (2018) 190:149 https://doi.org/10.1007/s10661-018-6507-8 A. Golkarian (*) Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran e-mail: golkarian@um.ac.ir S. A. Naghibi Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran B. Kalantar Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia B. Pradhan School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, 2007, PO Box 123, CB11.06.217, Building 11, 81 Broadway, Ultimo, Sydney, NSW, Australia B. Pradhan Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro Gwangjin-gu, Seoul 05006, , Republic of Korea