South Korea experiences numerous local sedimentation problems, such as landslides, upland erosion, aggradation and degradation, and flood plain sediment deposition. This has necessitated the development of a reliable and consistent approach for modeling sediment processes in the country. In this study, samples obtained from 35 gauging stations at five alluvial river basins in South Korea were used together with the modified Einstein procedure and series expansion of the modified Einstein procedure to determine the total sediment load at the sampling locations. Using two different methods, the total sediment load of majority of the 35 considered rivers were found to be typically 50–300 ton/km²·yr. A model tree data mining technique was used to develop a model for estimating the specific degradation based on certain meaningful parameters, namely, the 1) elevation at the middle relative area of the hypsometric curve [m], 2) percentage of wetland and water, 3) percentage of urban land, 4) mean annual precipitation [mm], 5) main stream length [km], and 6) watershed form factor [km²/km²]. The root mean square error of the predictions of the proposed model was found to be 55 ton/km²·yr less than those of existing statistical models. Erosion loss maps obtained by the revised universal soil loss equation (RUSLE), satellite images, and aerial photographs were also used to represent the geospatial features affecting erosion and sedimentation. The results of the geospatial analysis indicated that the transport of sediment into the alluvial rivers was affected by the wetlands located near the rivers, and also enabled clear delineation of the unique erosion features of construction sites in the urban areas. In addition, the watershed morphometric characteristics could be used to accurately represent the sediment transport. The proposed data mining methodology promises to facilitate the solution of various erosion and sedimentation problems in South Korea. The geospatial analysis procedure would also enable the understanding of spatially varied erosion and sedimentation processes under different conditions.

韩国遇到许多当地沉积问题，例如滑坡，山地侵蚀，水土流失和退化以及洪泛平原沉积物沉积。这就需要开发一种可靠且一致的方法来模拟该国的沉积过程。在这项研究中，将从韩国五个冲积河流域的35个测量站获得的样本与改进的爱因斯坦程序和改进的爱因斯坦程序的系列扩展一起使用，以确定采样位置的总泥沙量。使用两种不同的方法，发现35条被考虑河流中的大多数河流的总沉积物负荷通常为50-300吨/ km ²·年。使用模型树数据挖掘技术，基于某些有意义的参数来开发模型，以估算特定的退化，这些参数包括：1）湿度曲线[m]的中部相对区域的海拔； 2）湿地和水的百分比； 3）城市土地的百分比，4）年平均降水量[mm]，5）主干长度[km]，以及6）流域形状系数[km ² / km ² ]。发现所提出模型的预测的均方根误差为55 ton / km ²·yr比现有的统计模型少。通过修订的通用土壤流失方程（RUSLE）获得的侵蚀损失图，卫星图像和航空照片也被用来代表影响侵蚀和沉积的地理空间特征。地理空间分析的结果表明，沉积物向冲积河流的迁移受到河流附近湿地的影响，也使人们能够清晰地描绘出城市建筑工地的独特侵蚀特征。另外，分水岭的形态特征可以用来精确地表示沉积物的迁移。拟议中的数据挖掘方法有望促进解决韩国各种侵蚀和沉积问题。