토양 내 오염물질 농도 예측을 위한 베이지안 벌점 스플라인

Abstract

For the diagnosis of soil contamination, it is necessary to accurately understand the distribution of pollutants in the soil of the survey site. However, since it is practically impossible to investigate all observable points in the survey site, the concentration of pollutants in the soil of the survey site is predicted through a spatial prediction model based on data obtained from some selected points in the survey site. However, due to the nature of the soil data, the size of the data is often insufficient, which can greatly reduce the accuracy of spatial prediction. Therefore, in this study, to solve this problem, we propose a Bayesian penalized spline model that predicts the concentration of contaminants in the soil of the survey site by using the soil quality monitoring network data provided by the Ministry of Environment(MOE) in Korea as prior information. In addition, in order to evaluate the performance of the proposed model, RMSE, MAE, MAPE were used to compare the prediction accuracy by data size with several comparative models. As a result of the performance evaluation, it was confirmed that the performance of the proposed model showed better performance than the comparative models. In particular, the smaller the data size, the better the performance of the proposed model compared to the comparative models. Therefore, the use of the proposed model can be considered when trying to understand the distribution of pollutants in the soil at the initial stage of soil survey, where soil data for the survey site is relatively scarce.