Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods

Watershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discrimi...
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Autor*in:	Kardos Máté Krisztián [verfasserIn] Clement Adrienne [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	binary logistic regression diffuse pollution land use linear discriminant analysis point source pollution water quality monitoring water framework directive central europe

Übergeordnetes Werk:	In: Open Geosciences - De Gruyter, 2015, 12(2020), 1, Seite 71-84
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:1 ; pages:71-84

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/geo-2020-0006

Katalog-ID:	DOAJ00792142X

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callnumber-first	Q - Science
author	Kardos Máté Krisztián
spellingShingle	Kardos Máté Krisztián misc QE1-996.5 misc binary logistic regression misc diffuse pollution misc land use misc linear discriminant analysis misc point source pollution misc water quality monitoring misc water framework directive misc central europe misc Geology Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
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topic_title	QE1-996.5 Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods binary logistic regression diffuse pollution land use linear discriminant analysis point source pollution water quality monitoring water framework directive central europe
topic	misc QE1-996.5 misc binary logistic regression misc diffuse pollution misc land use misc linear discriminant analysis misc point source pollution misc water quality monitoring misc water framework directive misc central europe misc Geology
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title	Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
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title_full	Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
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title_sort	predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
callnumber	QE1-996.5
title_auth	Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
abstract	Watershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discriminant analysis models are trained to predict the physico-chemical status class on a five-class scale.
abstractGer	Watershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discriminant analysis models are trained to predict the physico-chemical status class on a five-class scale.
abstract_unstemmed	Watershed area and a bunch of relief, land use, and wastewater characteristics for 32 upland and 33 lowland small river courses are generated. Based on these characteristics, logistic binary regression models are trained to predict if the river achieves the good physico-chemical status, and discriminant analysis models are trained to predict the physico-chemical status class on a five-class scale.
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title_short	Predicting small water courses’ physico-chemical status from watershed characteristics with two multivariate statistical methods
url	https://doi.org/10.1515/geo-2020-0006 https://doaj.org/article/532b1edca85043659b82fa0e9e16ad90 https://doaj.org/toc/2391-5447
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up_date	2024-07-03T14:55:49.091Z
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