Application of Bipartite Networks to the Study of Water Quality

Pineda-Pineda, Jair J.; Martinez-Martinez, C. T.; Méndez-Bermúdez, J. A.; Muñoz‐Rojas, Jesús; Sigarreta, José M.

doi:10.3390/su12125143

Cited by 12 publications

(11 citation statements)

References 52 publications

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“…JP Index proposed and studied in detail by Pineda et al [25,26] is based on a bipartite network G = (V, E) where the vertex set V is divided in two disjoint sets T and A. The vertices in T = {t 1 , t 2 , .…”

Section: Jp(g) Indexmentioning

confidence: 99%

“…The following inequality from [25] with lower and upper bound on index I 1 (G), will be used later for the analysis of our final index:…”

Section: Analyzing the MMImentioning

confidence: 99%

“…In general, the studies that evaluate water quality classify this quality in five classes, commonly, very high, high, regular, low and very low (see [16,25] and its references). To classify the status in our study, the class width is determined by the quotient between the expected value of the I f (G) index at a reference site and the number of classes.…”

Section: Corollarymentioning

confidence: 99%

“…We use two of the basis indices of our index and the Shannon diversity index, which is one of the most commonly used ones in this type of study. The basis indices used were JP(G) and MIR index (see [15,25]).…”

Section: Comparison Between Indices To Study Water Qualitymentioning

confidence: 99%

“…[2]), the concept has been applied to more and more biological systems, including wetland plants and terrestrial invertebrates, and they have applied on a range of spatial scales from local to continental. In this sense, several MMIs have been developed with the aim of evaluating lagoons health (see [19,[23][24][25] and references therein). These MMIs have in common that they use a single indicator, which reduces the capacity to relate biotic and abiotic elements.…”

Section: Introductionmentioning

confidence: 99%

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Multimetric Index to Evaluate Water Quality in Lagoons: A Biological and Geomorphological Approach

et al. 2021

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In recent years, Multimetric Indices (MMIs) have received a lot of attention thanks to their ability to develop integrative evaluations of water quality, particularly in lagoons. In this article, we propose a new MMI for determining the water quality in lagoons. The proposed index is composed of biotic and abiotic indicators, in particular macroinvertebrates, macrophytes and morphological indicators. The proposed index is based on a geometric representation of a phenomenon associated with an ecological system, the ecosystem elements are mapped as vertices of a network and the relationship between them is represented by the corresponding edges. We classify the status of water bodies, from very low to very high using the ecological quality ratio. We compare our index with different different indices that measure water quality, such as General Biotic Index (JP(G)), Macrophyte Index for River (MIR) and Shannon diversity index (H’) and validate our index with Pearson’s correlation coefficient. A strong correlation with the JP(G) and MIR indices (R2 = 0.8605 and R2=0.7661, respectively) is obtained. Although the proposed index is composed of other indices, the independence of the proposed index with respect to its component indices is proven and the structure of the geometric model associated to the proposed network is studied. A close relationship between the measure called medium articulation and the geometric model associated with the proposed index is highlighted, which allows to determine the missing relationships in the network using structural analysis. The proposed index presents a more comprehensive measure than most indices currently used and has the advantage in the scalability, since other existing indicators can be integrated into our model.

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Section: Jp(g) Indexmentioning

confidence: 99%

“…The following inequality from [25] with lower and upper bound on index I 1 (G), will be used later for the analysis of our final index:…”

Section: Analyzing the MMImentioning

confidence: 99%

Section: Corollarymentioning

confidence: 99%

Section: Comparison Between Indices To Study Water Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multimetric Index to Evaluate Water Quality in Lagoons: A Biological and Geomorphological Approach

et al. 2021

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Geometric properties of chemical graphs

Rada

Rodrı́guez

Sigarreta

2021

Int J of Quantum Chemistry

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The study of Gromov hyperbolic graphs has many applications. In this paper we study the hyperbolicity constant of hexagonal systems. In particular, we compute the hyperbolicity constant of every catacondensed hexagonal system. Besides, we obtain upper and lower bounds of general hexagonal systems. Since the hyperbolicity constant of a graph measures the deviation of the graph from a tree, we also study the chemical graphs with small hyperbolicity constant.

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Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

Aswal,

Prasad,

Singh

et al. 2024

Sci Rep

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Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L−1) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.

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Application of Bipartite Networks to the Study of Water Quality

Cited by 12 publications

References 52 publications

Multimetric Index to Evaluate Water Quality in Lagoons: A Biological and Geomorphological Approach

Multimetric Index to Evaluate Water Quality in Lagoons: A Biological and Geomorphological Approach

Geometric properties of chemical graphs

Spatial analysis and soft computational modeling for hazard assessment of potential toxic elements in potable groundwater

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