Groundwater potential mapping using a novel data-mining ensemble model

• Published in: Hydrogeology journal Vol. 27; no. 1; pp. 211 - 224
• Main Authors: Kordestani, Mojtaba Dolat, Naghibi, Seyed Amir, Hashemi, Hossein, Ahmadi, Kourosh, Kalantar, Bahareh, Pradhan, Biswajeet
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2019; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Aquifers; Arid regions; Cartography; Data mining; Data processing; Earth and Environmental Science; Earth Sciences; Exploration; Freshwater; Freshwater resources; Geology; Geophysics/Geodesy; Groundwater; Groundwater availability; Groundwater potential; Hydrogeology; Hydrology/Water Resources; Inland water environment; Mapping; Mathematical models; Methods; Modelling; Poverty; Receptors; Regression analysis; Resource availability; Spring; Statistical analysis; Training; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Water resources; Water scarcity; Iran; Groundwater management; Geographic information system (GIS); Iran; Data mining
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-018-1848-5

Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.