Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals

Yongsung Joo, Keunbaik Lee, Joong Hyuk Min, Seong Taek Yun, Trevor H Park

Research output: Contribution to journalArticle

Abstract

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 (NO3-) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO3-) 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.

Original languageEnglish (US)
Pages (from-to)499-514
Number of pages16
JournalEnvironmetrics
Volume18
Issue number5
DOIs
StatePublished - Aug 1 2007

Fingerprint

Multivariate Regression
agrochemical
Water Quality
Logistics
logistics
water quality
Water
water
fertilizer
Mixture of Normal Distributions
Multivariate Models
Nitrate
Cluster Analysis
analysis
Pollution
Nitrogen
lime
cluster analysis
Regression Model
nitrate

Keywords

  • Agricultural pollution
  • ECM
  • Mixture of normal distributions
  • Mixture of regressions
  • Model-based clustering
  • Water quality

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecological Modeling

Cite this

Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals. / Joo, Yongsung; Lee, Keunbaik; Min, Joong Hyuk; Yun, Seong Taek; Park, Trevor H.

In: Environmetrics, Vol. 18, No. 5, 01.08.2007, p. 499-514.

Research output: Contribution to journalArticle

Joo, Yongsung ; Lee, Keunbaik ; Min, Joong Hyuk ; Yun, Seong Taek ; Park, Trevor H. / Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals. In: Environmetrics. 2007 ; Vol. 18, No. 5. pp. 499-514.
@article{cf5e6dff9a3046749e18bf164492e6d3,
title = "Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals",
abstract = "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 (NO3-) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO3-) 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.",
keywords = "Agricultural pollution, ECM, Mixture of normal distributions, Mixture of regressions, Model-based clustering, Water quality",
author = "Yongsung Joo and Keunbaik Lee and Min, {Joong Hyuk} and Yun, {Seong Taek} and Park, {Trevor H}",
year = "2007",
month = "8",
day = "1",
doi = "10.1002/env.820",
language = "English (US)",
volume = "18",
pages = "499--514",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

TY - JOUR

T1 - Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals

AU - Joo, Yongsung

AU - Lee, Keunbaik

AU - Min, Joong Hyuk

AU - Yun, Seong Taek

AU - Park, Trevor H

PY - 2007/8/1

Y1 - 2007/8/1

N2 - 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 (NO3-) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO3-) 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.

AB - 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 (NO3-) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO3-) 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.

KW - Agricultural pollution

KW - ECM

KW - Mixture of normal distributions

KW - Mixture of regressions

KW - Model-based clustering

KW - Water quality

UR - http://www.scopus.com/inward/record.url?scp=34447536202&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34447536202&partnerID=8YFLogxK

U2 - 10.1002/env.820

DO - 10.1002/env.820

M3 - Article

AN - SCOPUS:34447536202

VL - 18

SP - 499

EP - 514

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 5

ER -