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Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals
Author(s) -
Joo Yongsung,
Lee Keunbaik,
Min JoongHyuk,
Yun SeongTaek,
Park Trevor
Publication year - 2007
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.820
Subject(s) - environmental science , water quality , agrochemical , multivariate statistics , logistic regression , agriculture , cluster (spacecraft) , nitrate , pollution , lime , fertilizer , statistics , water resource management , hydrology (agriculture) , mathematics , geography , agronomy , ecology , computer science , engineering , materials science , geotechnical engineering , archaeology , programming language , metallurgy , biology
In this paper, we study the impacts of two representative agricultural activities, fertilizers and lime application, on water quality. Because of heavy usage of nitrogen fertilizers, nitrate (NO 3 − ) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO 3 − ) concentrations to cluster water samples into two environmentally interested groups (water impacted by agrochemicals and natural background water groups). However, this method fails to yield satisfying results because it cannot distinguish low‐level fertilizer impact and natural background noise. To improve performance of cluster analysis, we introduce the logistic mixture of multivariate regressions model (LMMR). In this approach, water samples are clustered based on the relationships between major element concentrations and physicochemical variables, which are different in impacted water and natural background water. Copyright © 2006 John Wiley & Sons, Ltd.