标题：Novel Hybrid Integration Approach of Bagging-Based Fisher's Linear Discriminant Function for Groundwater Potential Analysis
作者：Chen, Wei; Pradhan, Biswajeet; Li, Shaojun; Shahabi, Himan; Rizeei, Hossein Mojaddadi; Hou, Enke; Wang, Shengquan
作者机构：[Chen, Wei; Hou, Enke; Wang, Shengquan] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China.; [Chen, Wei] Shandong 更多
通讯作者：Pradhan, B;Pradhan, B
通讯作者地址：[Pradhan, B]Univ Technol Sydney, CAMGIS, Fac Engn & IT, Sydney, NSW 2007, Australia;[Pradhan, B]Sejong Univ, Dept Energy & Mineral Resources Engn, 209 更多
来源：NATURAL RESOURCES RESEARCH
关键词：Groundwater; Machine learning; Fisher's linear discriminant function; (FLDA); Rotation forest (RTF); GIS
摘要：Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher's linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.