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• W2920797498 abstract "Abstract Groundwater is one of the most beneficial natural resources worldwide. The main aim of the present study is the mapping of groundwater potential of the Zhangjiamao area in China using two novel hybrid data mining techniques that involve the adaptive neuro-fuzzy inference system (ANFIS) ensembled with teaching-learning-based optimization (TLBO) and biogeography-based optimization (BBO). First, 93 spring locations were identified in the study area, which were randomly divided in the ratio of 70:30 to be used for building models and validation, respectively. A total of 16 spring affecting factors viz., slope aspect, altitude, slope angle, plan curvature, profile curvature, curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to rivers, rainfall, lithology, soil, NDVI, and land use/land cover, were used as input variables. The probability certainty factor (PCF) method was applied for the correlation analysis between spring occurrences and the affecting factors. Subsequently, the ANFIS-TLBO and ANFIS-BBO models were generated to produce groundwater spring potential maps (GSPMs). Finally, the GSPMs were evaluated by using the area under the receiver operating characteristic (AUROC). The results showed that the AUROC values for the ANFIS-TLBO model based on the training and validation datasets were 0.866 and 0.905, respectively. In contrast, the AUROC values for the ANFIS-BBO model based on the training and validation datasets were 0.861 and 0.887, respectively. The results of ANFIS-TLBO showed that 25.66% of the study area could be classified into the high and very high groundwater spring potential classes as compared to the ANFIS-BBO model (30.52%). The results of the present study can be useful for groundwater management." @default.
• W2920797498 created "2019-03-22" @default.
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• W2920797498 date "2019-05-01" @default.
• W2920797498 modified "2023-10-17" @default.
• W2920797498 title "Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization" @default.
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• W2920797498 doi "https://doi.org/10.1016/j.jhydrol.2019.03.013" @default.
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