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Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria

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@article{IJASEIT13598,
   author = {Camilo Enrique Rocha Calderón and Octavio José Salcedo Parra and Sebastián Camilo Vanegas Ayala},
   title = {Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2369--2374},
   keywords = {Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.},
   abstract = {This document shows the results for two water quality-related trials based on the Physico-chemical characteristics given by the used dataset; both trials were carried out based on the same dataset from which the membership sets, and functions were defined the most relevant features. The first trial was a neural network method aimed to predict water quality through attributes as the pH, temperature, turbidity, salinity, among others; the second trial was a fuzzy logic system method for the detection of the Vibrio Cholerae in the water through the usual variables associated to its presence: temperature, salinity, phosphates, and nitrites' levels. The method for this research is divided into two phases. The first phase is developing suitable software using an iterative and incremental process model based on prototypes. The second phase or operative phase has an experimental characterization that allows for an adequation of the environment to establish the main features and properties that are relevant to the study object. The results showed effectiveness values of 99.99% (highest obtained value) for trial one and 70.23% for trial two; such values depict an accurate prediction on the quality of water and a valuable detection for Cholera related bacteria in water supplies. This research developed two highly interpretable and transparent systems to people through the graphic of the correspondences between the rules established and the membership functions in the input and output sets.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13598},
   doi = {10.18517/ijaseit.11.6.13598}
}

EndNote

%A Rocha Calderón, Camilo Enrique
%A Parra, Octavio José Salcedo
%A Ayala, Sebastián Camilo Vanegas
%D 2021
%T Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria
%B 2021
%9 Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.
%! Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria
%K Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.
%X This document shows the results for two water quality-related trials based on the Physico-chemical characteristics given by the used dataset; both trials were carried out based on the same dataset from which the membership sets, and functions were defined the most relevant features. The first trial was a neural network method aimed to predict water quality through attributes as the pH, temperature, turbidity, salinity, among others; the second trial was a fuzzy logic system method for the detection of the Vibrio Cholerae in the water through the usual variables associated to its presence: temperature, salinity, phosphates, and nitrites' levels. The method for this research is divided into two phases. The first phase is developing suitable software using an iterative and incremental process model based on prototypes. The second phase or operative phase has an experimental characterization that allows for an adequation of the environment to establish the main features and properties that are relevant to the study object. The results showed effectiveness values of 99.99% (highest obtained value) for trial one and 70.23% for trial two; such values depict an accurate prediction on the quality of water and a valuable detection for Cholera related bacteria in water supplies. This research developed two highly interpretable and transparent systems to people through the graphic of the correspondences between the rules established and the membership functions in the input and output sets.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13598
%R doi:10.18517/ijaseit.11.6.13598
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 6
%@ 2088-5334

IEEE

Camilo Enrique Rocha Calderón,Octavio José Salcedo Parra and Sebastián Camilo Vanegas Ayala,"Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2369-2374, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.13598.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rocha Calderón, Camilo Enrique
AU  - Parra, Octavio José Salcedo
AU  - Ayala, Sebastián Camilo Vanegas
PY  - 2021
TI  - Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2369
EP  - 2374
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.
N2  - This document shows the results for two water quality-related trials based on the Physico-chemical characteristics given by the used dataset; both trials were carried out based on the same dataset from which the membership sets, and functions were defined the most relevant features. The first trial was a neural network method aimed to predict water quality through attributes as the pH, temperature, turbidity, salinity, among others; the second trial was a fuzzy logic system method for the detection of the Vibrio Cholerae in the water through the usual variables associated to its presence: temperature, salinity, phosphates, and nitrites' levels. The method for this research is divided into two phases. The first phase is developing suitable software using an iterative and incremental process model based on prototypes. The second phase or operative phase has an experimental characterization that allows for an adequation of the environment to establish the main features and properties that are relevant to the study object. The results showed effectiveness values of 99.99% (highest obtained value) for trial one and 70.23% for trial two; such values depict an accurate prediction on the quality of water and a valuable detection for Cholera related bacteria in water supplies. This research developed two highly interpretable and transparent systems to people through the graphic of the correspondences between the rules established and the membership functions in the input and output sets.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13598
DO  - 10.18517/ijaseit.11.6.13598

RefWorks

RT Journal Article
ID 13598
A1 Rocha Calderón, Camilo Enrique
A1 Parra, Octavio José Salcedo
A1 Ayala, Sebastián Camilo Vanegas
T1 Water Quality Prediction and Detection of the Vibrio Cholerae Bacteria
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2369
OP 2374
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Fuzzy systems; neural networks; quality; Vibrio Cholerae; water.
AB This document shows the results for two water quality-related trials based on the Physico-chemical characteristics given by the used dataset; both trials were carried out based on the same dataset from which the membership sets, and functions were defined the most relevant features. The first trial was a neural network method aimed to predict water quality through attributes as the pH, temperature, turbidity, salinity, among others; the second trial was a fuzzy logic system method for the detection of the Vibrio Cholerae in the water through the usual variables associated to its presence: temperature, salinity, phosphates, and nitrites' levels. The method for this research is divided into two phases. The first phase is developing suitable software using an iterative and incremental process model based on prototypes. The second phase or operative phase has an experimental characterization that allows for an adequation of the environment to establish the main features and properties that are relevant to the study object. The results showed effectiveness values of 99.99% (highest obtained value) for trial one and 70.23% for trial two; such values depict an accurate prediction on the quality of water and a valuable detection for Cholera related bacteria in water supplies. This research developed two highly interpretable and transparent systems to people through the graphic of the correspondences between the rules established and the membership functions in the input and output sets.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13598
DO  - 10.18517/ijaseit.11.6.13598