Evaluation of a Depth-Based Multivariate k-Nearest Neighbor Resampling Method with Stormwater Quality Data. (7th May 2014)

• Record Type: Journal Article
• Authors: Lee, Taesam; Ouarda, Taha B. M. J.; Chebana, Fateh; Park, Daeryong
• Other Names: Sapountzakis Evangelos J. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-05-07
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/404198
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : A nonparametric simulation model (k -nearest neighbor resampling, KNNR) for water quality analysis involving geographic information is suggested to overcome the drawbacks of parametric models. Geographic information is, however, not appropriately handled in the KNNR nonparametric model. In the current study, we introduce a novel statistical notion, called a "depth function, " in the classical KNNR model to appropriately manipulate geographic information in simulating stormwater quality. An application is presented for a case study of the total suspended solids throughout the entire United States. The stormwater total suspended solids concentration data indicated that the proposed model significantly improves the simulation performance compared with the existing KNNR model.