Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

• Published in: Environmental monitoring and assessment Vol. 190; no. 3; p. 149
• Main Authors: Golkarian, Ali, Naghibi, Seyed Amir, Kalantar, Bahareh, Pradhan, Biswajeet
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2018; Springer Nature B.V
• Subjects: Academic disciplines; Algorithms; Altitude; Atmospheric Protection/Air Quality Control/Air Pollution; Climatic conditions; climatic factors; computer software; Curvature; data collection; Data mining; Datasets; Desert Climate; Discriminant analysis; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring - methods; Geographic Information Systems; Geographical information systems; Groundwater; Groundwater - analysis; Groundwater potential; Groundwater resources; Hydrology; Iran; Knowledge management; Land use; Literature reviews; Lithology; Models, Theoretical; Monitoring/Environmental Analysis; Multivariate Analysis; Regression Analysis; Resource exploitation; Rivers; Rivers - chemistry; ROC Curve; Sea level; Software; Spline functions; Splines; spring; Statistical analysis; Topography; Water demand; Water resources; Water Resources - supply & distribution; Water resources management; Water springs; Watersheds; Wetness index; Iran; Mapping; Iran; Geographic information system; Modeling; R statistical software
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-018-6507-8

Ever increasing demand for water resources for different purposes makes it essential to have better understanding 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, topographic 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 algorithms 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 calculated. 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 conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.