The river environment where people, animals, and plants exist together is a significant place to continue their own lives. Especially, since the river water quality directly impacts the survival of living things, it is crucial to effectively manage the quality of river water. To manage the river water quality effectively, it is important to make appropriate water quality management plans by accurately predicting the river water quality. Many researchers have utilized various tools for modelling the water quality of the river environment. Until now, river water quality has been modelled using the watershed model such as Soil and Water Assessment Tool (SWAT) [1], Hydrological Simulation Program-Fortran (HSPF) [2], and QUAL2E [3]. However, those models are developed in the US government (United States Department of Agriculture and United States Environmental Protection Agency), so it is challenging work to adapt those models to Korean watershed direct. And nowadays, the application of Artificial Intelligence (AI) is gradually increasing, because of its high prediction accuracy, adaptability for non-linearity, and high speed rather than other methodologies Despite the increasing use of AI in river water quality modelling, a challenge is that AI requires high-resolution dataset for effective modelling. However, in Korea, the resolution of the dataset for water quality of river environment is low because of lack of the number of conducted water quality monitoring stations.