A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models. Issue 7 (3rd July 2018)

• Record Type: Journal Article
• Authors: Hou, Enke; Wang, Jiale; Chen, Wei
• Is Part Of: Geocarto international. Volume 33:Issue 7(2018)
• Page Start: 754
• Page End: 769
• Publication Date: 2018-07-03
• Subjects: Groundwater spring potential -- statistical index -- index of entropy -- certainty factors -- China; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2017.1299801
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: The main aim of this study is to generate groundwater spring potential maps for the Ningtiaota area (China) using three statistical models namely statistical index (SI), index of entropy (IOE) and certainty factors (CF) models. Firstly, 66 spring locations were identified by field surveys, out of which, 46 (70%) spring locations were randomly selected for training the models and the rest 20 (30%) spring locations were used for validation. Secondly, 12 spring influencing factors, namely slope angle, slope aspect, altitude, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to streams, lithology and normalized difference vegetation index (NDVI) were derived from the spatial database. Subsequently, using the mentioned factors and the three models, groundwater spring potential values were calculated and the results were plotted in ArcGIS 10.0. Finally, the area under the curve was used to validate groundwater spring potential maps. The results showed that the IOE model, with the highest success rate of 0.9126 and the highest prediction rate of 0.9051, showed the preferable performance in this study. The results of this study may be helpful for planners and engineers in groundwater resource management and other similar watersheds.