Treated water quality assurance and description of distribution networks by multivariate chemometrics

Smeti, Eleni M.; Thanasoulias, N.C.; Lytras, Efthymios; Tzoumerkas, P.C.; Golfínopoulos, Spyros K.

doi:10.1016/j.watres.2009.07.023

Cited by 29 publications

(10 citation statements)

References 24 publications

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“…The analyses prove that when the missing data do not exceed 5 % of the set, satisfactory results of CART classification can be expected (Yong 2006). Thanks to its transparence and high accuracy, the method is used in various branches of science, e.g., medicine (Ture et al 2009), photogrammetry (Yang et al 2003), economics (Chang 2011), environmental protection (Smeti et al 2009), food science (Hazir et al 2012) and chemistry (Chudzinska and Baralkiewicz 2011). The CART classification is made in a few stages.…”

Section: Cart Backgroundmentioning

confidence: 94%

Classification and regression tree theory application for assessment of building damage caused by surface deformation

Malinowska

2014

Nat Hazards

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A framework of applying the classification and regression tree theory (CART) for assessing the concrete building damage, caused by surface deformation, is proposed. The prognosis methods used for approximated building hazard estimation caused by continuous deformation are unsatisfactory. Variable local soil condition, changing intensity of the continuous deformation and variable resistance of the concrete buildings require the prognosis method adapted to the local condition. Terrains intensely induced by surface deformation are build-up with hundreds of building, so the method of their hazard estimation needs to be approximated and relatively fast. Therefore, promising might be addressing problems of reliable building damage risk assessment by application of classification and regression tree. The presented method based on the classification and regression tree theory enables to establish the most significant risk factors causing the building damage. Chosen risk factors underlie foundation for the concrete building damage prognosis method, which was caused by the surface continuous deformation. The established method enabled to assess the severity of building damage and was adapted to the local condition. High accuracy of shown approach is validated based on the independent data set of the buildings from the similar region. The research presented introduces the CART to determination of the risk of building damage with the emphasis on the grade of the building damage. Since presented method bases on the observations of the damages from the previous subsidence, the method might be applied to any local condition, where the previous subsidence is known.

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Section: Cart Backgroundmentioning

confidence: 94%

Classification and regression tree theory application for assessment of building damage caused by surface deformation

Malinowska

2014

Nat Hazards

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“…Principal components analysis and/or factor analysis have been applied in a number of studies to identify groups of similar variables that characterize water contamination (Akbal et al 2010;Astel et al 2006;Chen et al 2007;Helena et al 2000;Facchinelli et al 2001;Juahir et al 2011;Pinto and Maheswari 2011;Reghunath et al 2002;Shrestha and Kazama 2007;Singh et al 2004;Soares et al 1999;Fig. 3 Dendrogram of the observed variables obtained by hierarchical clustering using Ward's method as a linkage method and Pearson correlation coefficient as a similarity measure Smeti et al 2009;Viera et al 2012). For the purpose of principal and factor analysis in our research, the original data were transformed by log(x+1) transformation.…”

Section: Principal Component and Factor Analysismentioning

confidence: 99%

“…Transformation by a previously used logarithmic function would result in exactly the same classification trees. In this study, we use the algorithm of classification and regression trees (CART), which was also applied in (Smeti et al 2009) for classification of samples of treated water according to its source. One of the first studies in which classification methods of artificial intelligence were applied for classification of water samples was conducted by Dzeroski and co-workers (1997), who used the CN2 algorithm for induction of decision trees in a biological classification of river water quality.…”

Section: Classification By Decision Treementioning

confidence: 99%

Evaluation of Spatial and Temporal Variation in Water Contamination Along Croatian Highways by Multivariate Exploratory Analysis

Dobša

Meznaric

Tompić

et al. 2014

Water Air Soil Pollut

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In the research, in this paper, we investigate spatial and temporal variations in the composition of wastewater near Croatian highways in three climatic regions (continental, Mediterranean, highland) during three seasons (autumn, winter and spring). In our paper, the spatial division of the investigated areas that pertain to the three aforementioned climatic regions was obtained using the method of hierarchical clustering of monitored locations. One thousand five hundred thirty-three samples from 14 locations along Croatian highways were collected and analysed by methods of multivariate exploratory analysis. By methods of principal components, factor analysis and hierarchical clustering of variables, we grouped the variables into factors. Whereas 60 % of variation in the data was explained by three principal components, six principal components accounted for 88 % of data variation. The key section of our research was conducted by the decision tree method. For the purpose of analysis, we classified 1,533 samples into three classes representing climatic regions separately for each season and obtained the accuracy of 76-90 % on test samples. Finally, using decision trees, we identified the most important variables that differentiate climatic regions by the level of contamination of water along highways in different seasons.

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“…Operations data management systems are already used by operations personnel to track operational data that could be used to establish the baseline operating conditions necessary for detection of contamination and calibration of hydraulics models of distribution and treatment systems (Serjeantson et al, 2011). Smeti et al (2009) used a statistical approach to predict water origin in a water system, suggesting that water quality data can be used to predict origin as it refers to a contamination location.…”

Section: Water Quality Parameters For Detection Of Contaminantsmentioning

confidence: 99%

Detection

Jain¹

2014

Drinking Water Security for Engineers, Planners, and Managers

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Treated water quality assurance and description of distribution networks by multivariate chemometrics

Cited by 29 publications

References 24 publications

Classification and regression tree theory application for assessment of building damage caused by surface deformation

Classification and regression tree theory application for assessment of building damage caused by surface deformation

Evaluation of Spatial and Temporal Variation in Water Contamination Along Croatian Highways by Multivariate Exploratory Analysis

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