Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter and Metal Ions in Waters from the Eastern Quadrilátero Ferrífero, Brazil

Gontijo, Erik Sartori Jeunon; Oliveira, S. D.; Fernandes, Mariana Lima; Silva, Gilmare A. da; Roeser, Hubert Matthias Peter; Friese, Kurt

doi:10.5935/0103-5053.20130285

Cited by 2 publications

(3 citation statements)

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“…For instance, CaD and MgD were highly correlated (0.94; Table S12 in the supporting information) and the variables map of both metals are similar indicating this relationship (Figure 3b). This similarity likely stems from a similar lithological origin (Gontijo et al, 2014).…”

Section: Discussionmentioning

confidence: 74%

“…The influence of variables of the groups formed (Figure 3a) is shown in their corresponding maps (Figure 3b). The red and blue colors in these maps scale the importance of specific variables to a sample or group of samples with red being more important (Garcia et al, 2007;Gontijo et al, 2014). Maps of samples that divide the samplings by depth and location individually are available in supporting information in Figure S3.…”

Section: Resultsmentioning

confidence: 99%

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Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Melo

Gontijo

Frascareli

et al. 2019

Water Resources Research

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Water quality in reservoirs is often compromised in many regions worldwide by nutrients and trace metals. This demands continuous monitoring; however, analyses of large data sets collected during regular monitoring remain a difficult task. Multivariate techniques offer a fast and robust approach for interpreting complex results. The objective of this study was to check the efficacy of selforganizing maps (SOMs) as a tool to investigate biogeochemical processes. This tool can also help to illustrate influences of land use patterns on the water quality of reservoirs. Here we use the Itupararanga Reservoir in Brazil as a subtropical example. Vertical profiles were sampled from seven sites in the reservoir in a total of seven campaigns over 24 months. Next to physicochemical parameters in the water column (dissolved oxygen, Eh, pH, and temperature), levels of nutrients (NO 3 − , NH 4 + , and PO 4 3− ), transition and trace metals (Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, and Zn), and chlorophyll-a (Chla) were measured. These variables were correlated with land use using SOM. With this technique samples were classified into 17 distinct groups that showed distinct influences of spatial heterogeneity and seasonality. The analyses helped to reveal a seasonal stratification period, where Fe, Mn, and P were released from sediments. Nutrients and some metal inputs (Al and Fe) were related to agricultural, urban, and grass/pasture areas around the reservoir. Our approach also helped to explain physical and biogeochemical seasonality in the reservoir.

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Section: Discussionmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Melo

Gontijo

Frascareli

et al. 2019

Water Resources Research

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“…Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used. Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13].…”

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confidence: 99%

Self-organized map: the new aproach for study of genetic divergence in kale

Brito

Júnior

Azevedo

et al. 2020

Preprint

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The objective of this study was to study the genetic divergence between genotypes of kale, to propose a methodology for the use of neural networks of the SOM type and to test its efficiency through Anderson's discriminant analysis. We evaluated 33 families of half-siblings of kale and three commercial cultivars. The design was a randomized block with four replications with six plants per plot. A total of 14 plant-level quantitative traits were evaluated. Genetic values were predicted at family level via REML / BLUP. For the study of divergence, neural networks of the SOM type (Selforganizing Map) were adopted. We evaluated different network architectures, whose consistencies of the clusters were identified by the Anderson discriminant analysis and by the number of empty clusters. After selecting the best network configuration, a dissimilarity matrix was obtained, from which a dendrogram was constructed using the UPGMA method. The best network architecture was formed with five rows and one column, totaling five neurons and consequently five clusters. The greatest dissimilarity was established between clusters I and V. The crossing between the genotypes of cluster I and those belonging to clusters III and V are the most recommended, since they aim to recombine families with characteristics of interest to the improvement and high dissimilarity. Anderson's discriminant analysis showed that the genotype classification was 100% correct, indicating the efficiency of the methodology used. Map (SOM) neural networks are a type of exploratory multivariate analysis tool that allows, through artificial computational intelligence, to design high-dimensional data in a smaller dimensional space, without loss of information [12]. This new organization prioritizes maintaining the structure, such as clusters and information relationships [13]. This reinforces its constant use in several

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Application of a Multivariate Exploratory Analysis Technique in the Study of Dissolved Organic Matter and Metal Ions in Waters from the Eastern Quadrilátero Ferrífero, Brazil

Cited by 2 publications

References 5 publications

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Self-organized map: the new aproach for study of genetic divergence in kale

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