Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique

Abstract Machine learning and data-driven models have achieved a favorable reputation in the field of advanced geospatial modeling, particularly for models of groundwater aquifer potential over large areas. Such models built using standalone machine learning techniques retain some uncertainty, including errors associated with the modeling process, sampling approach, and input hyper-parameters. Some of these techniques cannot be applied in data-scarce regions because high bias and variance can lead to oversimplification. Therefore, in the current study, we developed and validated a novel ensemble multi-adaptive boosting logistic regression (MABLR) model for groundwater aquifer potential mapping. This model was validated in a large area of the Gyeongsangbuk-do basin in South Korea and the results were compared to those of different types of machine learning models including multiple-layer perception (MPL), logistic regression (LR), and support vector machine (SVM) models. A forward stepwise LR technique was implemented to assess the importance of contributing morphological factors; we found 15 factors that contributed significantly: topographic wetness index (TWI), topographic roughness index (TRI), stream power index (SPI), topographic position index (TPI), multi-resolution valley bottom flatness (MVBF), slope, aspect, slope length (LS), distance from the river, distance from the fault, profile curvature, plane curvature, altitude, land use/land cover (LULC), and geology. We optimized the MABLR model using a fuzzy logic supervised (FLS) approach with 184 iterations and then validated the results using accuracy assessment metrics including the κ coefficient, root-mean-square error (RMSE), receiver operating characteristics (ROC), and the precision-recall curve (PRC). Our model had superior predictive performance among the models tested, with higher overall goodness-of-fit and validation values according to the κ coefficient (0.819 and 0.781, respectively), ROC (0.917 and 0.838), and PRC (0.931 and 0.872). Our experimental results demonstrate that MABLR is more effective at reducing bias and variance error than other constituent machine learning methods.

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