A subagging regression method for estimating the qualitative and quantitative state of groundwater | Méthode de régression par sous-échantillonnage et agrégation (subbaging) pour estimer l’état qualitatif et quantitative des eaux souterraines Un método regresión por submuestreo para estimar el estado cualitativo y cuantitativo del agua subterránea 估算地下水定性和定量状态的集成回归法 Um método de regressão por agregação de subamostra para estimar o estado qualitativo e quantitativo das águas subterrâneas
2017
Jeong, Jina | Park, Eungyu | Han, Weon Shik | Kim, Kue-Young
A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.
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