Envirometrics. Part I: Modeling of water salinity and air quality data

Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected...

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Bibliographic Details
Published in:	Annali di chimica Vol. 91; no. 1-2; p. 29
Main Authors:	Braibanti, A, Gollapalli, N R, Jonnalagaddaj, S B, Duvvuru, S, Rupenaguntla, S R
Format:	Journal Article
Language:	English
Published:	Italy 01-01-2001
Subjects:	Air Pollutants - analysis Environmental Monitoring - methods Fresh Water - analysis Least-Squares Analysis Models, Theoretical Multivariate Analysis New York North Carolina Water Pollutants, Chemical - analysis North Carolina New York
Online Access:	Get more information
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Summary:	Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected the outliers whereas linear least squares (LLS) could not detect and remove the outliers. The second data set consists of daily readings of air quality values. Outliers are detected by LMS and unbiased regression coefficients are estimated by multi-linear regression (MLR). As explanatory variables are not independent, principal component regression (PCR) and partial least squares regression (PLSR) are used. Both examples demonstrate the superiority of LMS over LLS.
ISSN:	0003-4592