Environmental indicators through artificial neural networks

Indicators are the most important management tool for environmental monitoring. Environmental indicators condense the information and simplify the approach to environmental phenomena, which are often complex, and makes them very useful for communication. The usefulness of these indicators consists o...

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Autores:
silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Jiménez - Rodríguez, Luis Miguel
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5956
Acceso en línea:
https://hdl.handle.net/11323/5956
https://repositorio.cuc.edu.co/
Palabra clave:
Artificial neural networks
Environmental indicators
Environmental monitoring
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/5956
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv Environmental indicators through artificial neural networks
title Environmental indicators through artificial neural networks
spellingShingle Environmental indicators through artificial neural networks
Artificial neural networks
Environmental indicators
Environmental monitoring
title_short Environmental indicators through artificial neural networks
title_full Environmental indicators through artificial neural networks
title_fullStr Environmental indicators through artificial neural networks
title_full_unstemmed Environmental indicators through artificial neural networks
title_sort Environmental indicators through artificial neural networks
dc.creator.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Jiménez - Rodríguez, Luis Miguel
dc.contributor.author.spa.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Jiménez - Rodríguez, Luis Miguel
dc.subject.spa.fl_str_mv Artificial neural networks
Environmental indicators
Environmental monitoring
topic Artificial neural networks
Environmental indicators
Environmental monitoring
description Indicators are the most important management tool for environmental monitoring. Environmental indicators condense the information and simplify the approach to environmental phenomena, which are often complex, and makes them very useful for communication. The usefulness of these indicators consists of providing relevant information, summarized in the form of concise and illustrative statements for decision making, both for the organization's management and for the rest of the members. The prediction of limit values, together with the potentialities offered by the recommendation system based on ontology make this system a powerful tool for supporting decision-making in the Environmental Management process with a wide possibility of generalization in the business sector.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-01-30T13:45:56Z
dc.date.available.none.fl_str_mv 2020-01-30T13:45:56Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv 10.1088/1742-6596/1432/1/012049/pdf
dc.relation.references.spa.fl_str_mv [1] Cios, K. J., & Kurgan, L. A. (2000). Trends in Data Mining and Knowledge Discovery. (Dm), 1- 26.
[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham
[3] Demsar, J. (2006). Comparison of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, vol. 7: 31.
[4] Hutt, S.; Gardener, M.; Kamentz, D.; Duckworth, A.; D'Mello, S.: Prospectively Predicting 4- year College Graduation from Student Applications. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 280-289 (2018)
[5] Ahuja, R.; Kankane, Y.: Predicting the probability of student's degree completion by using different data mining techniques. Fourth International Conference on Image Information Processing (ICIIP), pp. 1-4 (2017)
[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)
[7] IHOBE. (1999). Guía de Indicadores Medioambientales para la Empresa. Berlin: Ministerio Federal para el Medio Ambiente, la Conservación de la Naturaleza y la Seguridad Nuclear.
[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)
[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)
[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).
[11] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.
[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.
[13] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Computer Science , 151 , 1225- 1230.
[14] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham
[15] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.
[16] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognition.
[17] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).
[18] Castellanos Domíngez, M. I., Quevedo Castro, C. M., Vega Ramírez, A., Grangel González, I., & Moreno Rodríguez, R. (2016). Sistema basado en ontología para el apoyo a la toma de decisiones en el proceso de gestión ambiental empresarial. Paper presented at the II International Workshop of Semantic Web, La Habana, Cuba. http://ceur-ws.org/Vol-1797/
[19] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).
[20] Castellanos Domínguez, M. I., & Grangel González, I. (2013). Las ontologías, su uso para la gestión del conocimiento medioambiental. Paper presented at the III Taller Internacional la Matemática, la Informática y la Física en el Siglo XXI, Holguín.
[21] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.
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spelling silva d, jesus gSenior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamJiménez - Rodríguez, Luis Miguel2020-01-30T13:45:56Z2020-01-30T13:45:56Z20201742-65881742-6596https://hdl.handle.net/11323/5956Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Indicators are the most important management tool for environmental monitoring. Environmental indicators condense the information and simplify the approach to environmental phenomena, which are often complex, and makes them very useful for communication. The usefulness of these indicators consists of providing relevant information, summarized in the form of concise and illustrative statements for decision making, both for the organization's management and for the rest of the members. The prediction of limit values, together with the potentialities offered by the recommendation system based on ontology make this system a powerful tool for supporting decision-making in the Environmental Management process with a wide possibility of generalization in the business sector.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-600Senior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamJiménez - Rodríguez, Luis Miguel-will be generated-orcid-0000-0002-0386-8433-600engJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012049/pdf[1] Cios, K. J., & Kurgan, L. A. (2000). Trends in Data Mining and Knowledge Discovery. (Dm), 1- 26.[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[3] Demsar, J. (2006). Comparison of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, vol. 7: 31.[4] Hutt, S.; Gardener, M.; Kamentz, D.; Duckworth, A.; D'Mello, S.: Prospectively Predicting 4- year College Graduation from Student Applications. Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 280-289 (2018)[5] Ahuja, R.; Kankane, Y.: Predicting the probability of student's degree completion by using different data mining techniques. Fourth International Conference on Image Information Processing (ICIIP), pp. 1-4 (2017)[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)[7] IHOBE. (1999). Guía de Indicadores Medioambientales para la Empresa. Berlin: Ministerio Federal para el Medio Ambiente, la Conservación de la Naturaleza y la Seguridad Nuclear.[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).[11] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.[13] Viloria, A., & Lezama, O. B. P. (2019). An intelligent approach for the design and development of a personalized system of knowledge representation. Procedia Computer Science , 151 , 1225- 1230.[14] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[15] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.[16] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognition.[17] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).[18] Castellanos Domíngez, M. I., Quevedo Castro, C. M., Vega Ramírez, A., Grangel González, I., & Moreno Rodríguez, R. (2016). Sistema basado en ontología para el apoyo a la toma de decisiones en el proceso de gestión ambiental empresarial. Paper presented at the II International Workshop of Semantic Web, La Habana, Cuba. http://ceur-ws.org/Vol-1797/[19] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).[20] Castellanos Domínguez, M. I., & Grangel González, I. (2013). Las ontologías, su uso para la gestión del conocimiento medioambiental. Paper presented at the III Taller Internacional la Matemática, la Informática y la Física en el Siglo XXI, Holguín.[21] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Artificial neural networksEnvironmental indicatorsEnvironmental monitoringEnvironmental indicators through artificial neural networksArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALEnvironmental Indicators through Artificial Neural Networks.pdfEnvironmental Indicators through Artificial Neural Networks.pdfapplication/pdf643649https://repositorio.cuc.edu.co/bitstreams/4b869d4e-0572-49d9-abc6-d3ef3ea74267/download4ecb7512bfddbaa627a81dcce5d8ed39MD51Environmental Indicators through Artificial Neural Networks.pdfEnvironmental Indicators through Artificial Neural Networks.pdfapplication/pdf1510882https://repositorio.cuc.edu.co/bitstreams/ff9b4613-df9f-4711-a6b1-075b362e499d/downloadcba8a15324583e6c7edf87efac3c0a4fMD56CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/7169895c-bfe2-4e31-a4a4-a60602406183/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/4fe45d76-ca21-485e-8255-ad4df1e80259/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILEnvironmental Indicators through Artificial Neural Networks.pdf.jpgEnvironmental Indicators through Artificial Neural Networks.pdf.jpgimage/jpeg27267https://repositorio.cuc.edu.co/bitstreams/b191cd6a-a3e8-46b7-aff1-7a4b7ce51764/download27fc500a2590a5bb2d14b8ce6504c095MD55TEXTEnvironmental Indicators through Artificial Neural Networks.pdf.txtEnvironmental Indicators through Artificial Neural Networks.pdf.txttext/plain22247https://repositorio.cuc.edu.co/bitstreams/0a047f54-dd6f-40f0-906e-1366f1e84256/downloaddf13c0216b0116aaa770d10f3d035354MD5711323/5956oai:repositorio.cuc.edu.co:11323/59562024-09-17 14:12:55.283http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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