Evaluating Machine Learning Models for Predicting Heavy Metal Contamination in Sediments of South Korea's Four Major Rivers

Heavy metal contamination in river sediments poses serious risks to human health, particularly through the use of river water as a drinking source and the bioaccumulation of pollutants in aquatic ecosystems. Industrial wastewater discharge and soil erosion caused by rainfall introduce heavy metals into rivers. These metals undergo adsorption and deposition processes, accumulating in sediments where natural removal is exceedingly slow. Moreover, current sediment contamination assessments rely on direct sampling and chemical analysis, which are time-consuming and costly. To enable more efficient monitoring of heavy metals, there is a growing need for predictive modeling using machine learning techniques.

          This study aims to identify the optimal machine learning model for predicting heavy metal concentrations in river sediments. The target heavy metals include Zn, Cu, Ni, Cd, and Hg. For model development and validation, nine years of data from South Korea's four major rivers (Han, Nakdong, Yeongsan, and Geum Rivers) were utilized. Considering the imbalance in the dataset due to the distinct characteristics of heavy metal inflows from polluted wastewater discharges from industrial areas and other sources, preprocessing techniques such as Z-score normalization and MinMaxScaler were employed to standardize the data. Three approaches were evaluated: Convolutional Neural Networks (CNNs), Random Forest, and a hybrid CNN RF model combining CNN parameters with Random Forest. Among these, the Random Forest model demonstrated relatively higher accuracy than the others. By leveraging machine learning techniques, this study offers a practical alternative to traditional methods, overcoming temporal and spatial limitations while significantly reducing the time and costs associated with sediment monitoring.