Projection pursuit water quality evaluation model based on chicken swam algorithm

2018 ◽  
Author(s):  
Zhe Hu
Keyword(s):  
Water Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Projection Pursuit ◽  
Water Quality Evaluation ◽  
Model Based ◽  
Quality Evaluation Model

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

2015 ◽  
Vol 16 (3) ◽  
pp. 703-714 ◽  
Author(s):  
Yongdeok Cho
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Water Quality Evaluation ◽  
Watershed Characteristics ◽  
Land Usage ◽  
Watershed Models ◽  
Squared Error ◽  
Quality Evaluation Model

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.

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