Modelling eutrophication and microbial risks in peri-urban river systems using discriminant function analysis

Ultra high performance liquid chromatography with photodiode array detector and quadrupole time‐of‐flight tandem mass spectrometry coupled with discriminant analysis to evaluate Angelicae pubescentis radix from different regions

“…Second, a cutoff value is then calculated based on the group means of the explanatory variables called group centroid. Finally, a new case is predicted as belonging to a particular group based on discriminant function score and group centroid .…”

“…Discriminant analysis is a multivariate statistical technique that has been used successfully in various fields including medicine, business, and food industry . It was often used to classify each observation into groups by building a predictive model based on observed predictors.…”

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

“…Kuo et al (2007) used an artificial neural network to combine the key factors that influence water quality, such as DO, Chl-a, TP, and the Secchi disk depth, for eutrophication prediction in a reservoir in central Taiwan. Pinto et al (2012) developed a two-level discriminant function analysis model for rapidly assessing the eutrophication risk from three easy-tomeasure parameters (saturated dissolved oxygen, turbidity and temperature), providing approximately 72% prediction accuracy.…”

“…Currently, multivariate statistical approaches have been developed and applied to assess the interrelations among the various parameters related to eutrophication and to combine eutrophic effects with different aspects of the marine environment. For example, principal component analysis (PCA) has been used to determine the main variables that affect eutrophication processes from a wide number of water quality parameters including TN, TP, oxygen, Chl-a, Secchi depth, phosphate, nitrate, nitrite, and ammonia (Lundberg et al, 2005;Primpas et al, 2010); Cluster analysis (CA) has been used to classify waters into the three eutrophication statuses including the oligotrophic, mesotrophic, and eutrophic state using several variables (Chl-a, phosphate, nitrate, nitrite, and ammonia) (Stefanou et al, 2000;Primpas et al, 2008); Discriminant factor analysis (DFA) has been used to identify different variables (nitrate, phosphate, Chl-a, DO, turbidity and temperature) that can differentiate sampling sites and to group them according to their eutrophication conditions (Tsirtsis and Karydis, 1999;Pinto et al, 2012); Artificial neural network (ANN) mode has been used for prediction of eutrophication conditions with reasonable accuracy by a wide range of variables (TP, TN, COD, the Secchi disk depth, DO and Chl-a) (Jiang et al, 2006;Kuo et al, 2007). Support vector machine (SVM) (Vapnik, 1995) is a promising power approach used to reflect the nonlinearity between responsive indicator and input variables using stochastic error minimization approaches (Zhou et al, 2016a) and is an effective tool to predict values from a wide variety of environmental fields (Ribeiro and Torgo, 2008;Farfani et al, 2015;Kisi et al, 2015).…”

Salinization mechanisms of a small alluvial aquifer in the semiarid region of northeast Brazil

“…Many authors have used multivariate statistical analysis in an attempt to describe the hydrogeological processes with interactions of aquifer systems (Ji et al, 2007;Cloutier et al, 2008;Raspa et al, 2008;Pinto et al, 2012) and inverse geochemical modeling (Parkhurst & Appelo, 1999) aiming to describe and identify the hydrogeochemical processes of waterrock interaction (Uliana & Sharp, 2001;Mahlknecht et al, 2006;Sharif et al, 2008), since the weathering of rocks can play a relevant role in the chemistry of the water (Marković et al, 2013).…”