2022
DOI: 10.1080/1573062x.2022.2029913
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Assessing major drivers of runoff water quality using principal component analysis: a case study from a Colombian and a Brazilian catchments

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Cited by 8 publications
(7 citation statements)
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“…In PCA, the variances of the principal components are the eigenvalues of the covariance matrix that illustrates the correlations or relationships between the input variables. Hence, the larger the percentage of the variance, the more important the corresponding variable or it reveals and the more relevant information [ 32 ]. The variances for the input variables, including resin (Knittex RCT), polyethylene softener, catalyst (Knittex Mo), curing temperature, and curing time calculated from PCA are 632.0161, 73.2251, 64.7666, 18.2146, and 0.7824, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…In PCA, the variances of the principal components are the eigenvalues of the covariance matrix that illustrates the correlations or relationships between the input variables. Hence, the larger the percentage of the variance, the more important the corresponding variable or it reveals and the more relevant information [ 32 ]. The variances for the input variables, including resin (Knittex RCT), polyethylene softener, catalyst (Knittex Mo), curing temperature, and curing time calculated from PCA are 632.0161, 73.2251, 64.7666, 18.2146, and 0.7824, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…Filter strips and grass swales are commonly used as pre-treatment for sedimentation (Silva et al, 2022). Grass swales are shallow, wide channels that are lined with vegetation and designed to direct water towards infiltration areas or watercourses.…”
Section: Treatment Through Sedimentation For Ssssmentioning
confidence: 99%
“…An additional statistical procedure employed was Principal Component Analysis (PCA), used to assess the relationship between soil and rainfall, runoff and land use in the watershed aspect [24][25][26]. PCA employs an orthogonal transformation to convert a set of potentially correlated observations into a set of linearly uncorrelated variables.…”
Section: Statistical and Multivariate Data Analysismentioning
confidence: 99%
“…Additionally, it was utilized to analyze the relationships among precipitation events, surface runoff, and soil erosion on the Pisha sandstone slopes of the Loess Plateau in China, where it has been demonstrated that forests play a pivotal role in erosion control [25]. Therefore, PCA offers revelant insights for the modelling and management of stormwater within watersheds [24].…”
Section: Multivariate Analysis Of Rainfall Runoff and Land Usementioning
confidence: 99%