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Evaluation of a Depth-Based Multivariatek-Nearest Neighbor Resampling Method with Stormwater Quality Data
Author(s) -
Taesam Lee,
Taha B. M. J. Ouarda,
Fateh Chebana,
Daeryong Park
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/404198
Subject(s) - algorithm , resampling , nonparametric statistics , multivariate statistics , mathematics , computer science , data mining , statistics , machine learning
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

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