This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m³/d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

This study characterized nitrate-nitrogen (NO3-N) concentrations in groundwater and stream water in an agricultural head watershed in South Korea and identified the pollution load of NO3-N as a result of the groundwater entering streams using field surveys, analyses of chemical constituents, and numerical modeling. The mean NO3-N concentration in groundwater was 7.373 mg/L, which is approximately 1.9 times higher than concentrations found in stream water. The groundwater and stream water samples belonged to the Ca-HCO3 type. The concentration of NO3-N in groundwater tended to increase in the lowland areas downstream. There was seasonal variations of NO3-N in both the groundwater and stream water samples, with increases in concentration during the dry season (January-April) and decreases during the wet season (June-October). The NO3-N load in stream water to that in groundwater (R) was higher during the wet season (September) than the dry season (March), with R distinctly increasing in upstream areas relative to downstream areas, indicating that during the wet season, a large amount of NO3-N is introduced into stream water from groundwater. By analyzing the relationship between groundwater and stream water and through NO3-N transport modeling, it was revealed that in the watershed, the nitrate-N load in stream water is greatly augmented by inputs from groundwater, particularly in the middle and downstream areas.