A watershed water quality evaluation model using data mining as an alternative to physical watershed models

This paper presents a data mining (DM)-based approach to developing a watershed water quality evaluation model (water quality evaluation model based on data mining (WQEMD)) as an alternative to physical watershed models. Three DM techniques (i.e. model tree, artificial neural network, and radial basis function) were employed to develop a WQEMD based on watershed characteristics (e.g. hydrology, geology, and land usage). To represent watershed characteristics, three cases and ten scenarios were considered. The three cases were defined as (1) the size (area) allocation of sub-watersheds, (2) the watershed imperviousness ratio, and (3) the combination of the area and imperviousness ratio. The ten scenarios were composed of the following parameters; impervious, pervious, land usage, rainfall, slope. The best WQEMDs were subsequently developed using statistics (correlation coefficient, mean-absolute error, root mean-squared error, and root relative-squared error). In addition, the WQEMDs developed were then verified using the Geum-Sum-Youngsan River watershed. The percentage difference of biochemical oxygen demand (BOD), total nitrogen (T-N) and total phosphorus (T-P) were 30.6%, 23.44%, and 2.79%, respectively. The results show that a WQEMD developed in this way is effective and can be used in place of a physical watershed model and is useful to aid in determining areas having the best potential for successful remediation.