## 1 Introduction  
Soil erosion occurs when the shear force of the overland flow is greater than soil cohesion. The sediments generated by weathering are transported by natural media and deposited in rivers before or after reaching them. In this process, the sediments have a direct impact on the hydraulic function and cause multiple problems from the aspect of river maintenance and management [1]. For example, the sediment transfer can cause rise of flood, decrease stability of the embankment, and headward erosion [2; 3]. It can also cause infrastructure instability and changes in the ecological environment [4; 5]. Therefore, for the design, operation, and management of ecofriendly and sustainable rivers, it is instrumental to have a sufficient understanding of the discharge of inflow and sediment transport and quantitatively identify and predict the sediment yield [6; 7]. To solve erosion and sediment related problems, various models have been proposed for the estimation of the sediment yield or sediment transport rate [8; 9; 10]. Especially, numerous mathematical formulas based on statistical methods have focused on sediment yield quantification [11; 12; 13; 14]. However, with the present level of knowledge, elucidation, and accurate quantitative prediction of the complex process of sediment generation, transport, and deposition that must consider various factors, such as geography, topography, geology, geology, climatology, forestry, and hydraulics of rivers and basins, remain highly challenging [1]. The developed models for sediment yield are generally classified as physical, conceptual, and empirical, and an appropriate model is used in consideration of the purpose and conditions [7]. In the case of the empirical model used for prediction of sediment yield and its validation in this study, the relationship between various characteristics and sediment observation results were used. To develop an empirical model with excellent predictability, reliable measurement results are required. Even though the models were developed using reliable measurements, there are cases in which various empirical models that have been developed to date show a considerable difference even when subjected to the same gauging points and identical hydraulic conditions. Recently, active research has been conducted for the development of a sediment yield estimation model with excellent prediction performance using data mining techniques that allows systematic and automatic derivation of statistical rules and patterns from big data composed of various variables. [PERSON] [15] estimated the sediment concentration using an artificial neural network (ANN) model, and [PERSON] and [PERSON] [16] used an ANN model and numerical analysis in combination for analysis of suspended sediment transport. [PERSON] et al. [17] used an ANN model for sediment concentration estimation and compared the results with the values derived from existing formulas. [PERSON] et al. [18] performed comparative evaluation using an ANN and a model trees method for the estimation of sediment yield. [PERSON] et al. [19] employed ANN and wavelet-cum-ANN methods to estimate suspended sediment loads in the Elbe River in Germany. In addition, [PERSON] et al. [20] presented erosion susceptibility mapping through the development of a hybrid model that simultaneously utilized various data mining techniques. In South Korea, [PERSON] [13] developed a model to predict the total sediment load through the relationship between the river channel and hydraulic factors using the model tree method, one of the data mining techniques. Additionally, using the same technique, [PERSON] et al. [14] developed a model based on various watershed characteristics to predict specific degradation (SD). A data mining technique could be one of the approaches to predict sediment yield; however, this method only focuses on the accuracy of prediction. Therefore, the models based on this technique should consider appropriate hydraulic and hydrogeomorphic conditions, development purpose, and the range of data used.