Twitter data mining for the diagnosis of leaks in drinking water distribution networks

This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the proce...

Full description

Autores:
Jiménez-Cabas, Javier
Torres, Lizeth
Lozoya-Santos, Jorge de Jesús
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9964
Acceso en línea:
https://hdl.handle.net/11323/9964
https://repositorio.cuc.edu.co/
Palabra clave:
Leak diagnosis
Social sensors
Social network data
Twitter
Text mining
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_30a29c3683f4380ec839b9740bcea2f2
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9964
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Twitter data mining for the diagnosis of leaks in drinking water distribution networks
title Twitter data mining for the diagnosis of leaks in drinking water distribution networks
spellingShingle Twitter data mining for the diagnosis of leaks in drinking water distribution networks
Leak diagnosis
Social sensors
Social network data
Twitter
Text mining
title_short Twitter data mining for the diagnosis of leaks in drinking water distribution networks
title_full Twitter data mining for the diagnosis of leaks in drinking water distribution networks
title_fullStr Twitter data mining for the diagnosis of leaks in drinking water distribution networks
title_full_unstemmed Twitter data mining for the diagnosis of leaks in drinking water distribution networks
title_sort Twitter data mining for the diagnosis of leaks in drinking water distribution networks
dc.creator.fl_str_mv Jiménez-Cabas, Javier
Torres, Lizeth
Lozoya-Santos, Jorge de Jesús
dc.contributor.author.none.fl_str_mv Jiménez-Cabas, Javier
Torres, Lizeth
Lozoya-Santos, Jorge de Jesús
dc.subject.proposal.eng.fl_str_mv Leak diagnosis
Social sensors
Social network data
Twitter
Text mining
topic Leak diagnosis
Social sensors
Social network data
Twitter
Text mining
description This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-03-16T15:46:42Z
dc.date.available.none.fl_str_mv 2023-03-16T15:46:42Z
dc.date.issued.none.fl_str_mv 2023-03-14
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Jiménez-Cabas, J.; Torres, L.; Lozoya-Santos, J.J. Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks. Sustainability 2023, 15, 5113. https://doi.org/10.3390/ su15065113
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/9964
dc.identifier.doi.none.fl_str_mv 10.3390/ su15065113
dc.identifier.eissn.spa.fl_str_mv 2071-1050
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Jiménez-Cabas, J.; Torres, L.; Lozoya-Santos, J.J. Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks. Sustainability 2023, 15, 5113. https://doi.org/10.3390/ su15065113
10.3390/ su15065113
2071-1050
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9964
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sustainability
dc.relation.references.spa.fl_str_mv 1. Ling, T. A Global Study about Water Crisis. In Proceedings of the 2021 International Conference on Social Development and Media Communication (SDMC 2021), Sanya, China, 26–28 November 2021; Atlantis Press: Paris , France, 2022; pp. 809–814.
2. Briseño, H.; Sánchez, A. Decentralization, consolidation, and crisis of urban water management in Mexico. Tecnol. y Cienc. Del Agua 2018, 9, 25–47. [CrossRef]
3. Khalifa, D.S.; El Atty, A.; Donia, N.S.; Moussa, A.; Mohamed, A. Analysis and Assessment of Water Losses in Domestic Water Distribution Networks. J. Environ. Sci. 2022, 51, 1–23. [CrossRef]
4. Verde, C.; Torres, L. Modeling and Monitoring of Pipelines and Networks: Advanced Tools for Automatic Monitoring and Supervision of Pipelines; Springer: Berlin/Heidelberg, Germany, 2017; Volume 7.
5. Carpentier, P.; Cohen, G. State estimation and leak detection in water distribution networks. Civ. Eng. Syst. 1991, 8, 247–257. [CrossRef]
6. Pérez, R.; Puig, V.; Pascual, J.; Quevedo, J.; Landeros, E.; Peralta, A. Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Eng. Pract. 2011, 19, 1157–1167. [CrossRef]
7. Soldevila, A.; Blesa, J.; Tornil-Sin, S.; Duviella, E.; Fernandez-Canti, R.M.; Puig, V. Leak localization in water distribution networks using a mixed model-based/data-driven approach. Control Eng. Pract. 2016, 55, 162–173. [CrossRef]
8. Li, X.; Wen, Y.; Jiang, J.; Daim, T.; Huang, L. Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data. Technol. Forecast. Soc. Chang. 2022, 184, 122042. [CrossRef]
9. Sakaki, T.; Okazaki, M.; Matsuo, Y. Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA, 26–30 April 2010; pp. 851–860.
10. Jordan, S.E.; Hovet, S.E.; Fung, I.C.H.; Liang, H.; Fu, K.W.; Tse, Z.T.H. Using Twitter for public health surveillance from monitoring and prediction to public response. Data 2018, 4, 6. [CrossRef]
11. Bonifazi, G.; Breve, B.; Cirillo, S.; Corradini, E.; Virgili, L. Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach. Inf. Process. Manag. 2022, 59, 103095. [CrossRef]
12. Pascual-Ferrá, P.; Alperstein, N.; Barnett, D.J. Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication. Disaster Med. Public Health Prep. 2022, 16, 561–569. [CrossRef] [PubMed]
13. Pilaˇrová, L.; Kvasniˇcková Stanislavská, L.; Pilaˇr, L.; Balcarová, T.; Pitrová, J. Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats. Foods 2022, 11, 2695. [CrossRef] [PubMed]
14. Rahman, S.; Jahan, N.; Sadia, F.; Mahmud, I. Social crisis detection using Twitter based text mining-a machine learning approach. Bull. Electr. Eng. Inform. 2023, 12, 1069–1077. [CrossRef]
15. Qorib, M.; Oladunni, T.; Denis, M.; Ososanya, E.; Cotae, P. COVID-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination twitter dataset. Expert Syst. Appl. 2023, 212, 118715. [CrossRef]
16. Choi, Y.J.C.E.J. The Early Emotional Responses and Central Issues of People in the Epicenter of the COVID-19 Pandemic: An Analysis from Twitter Text Mining. Int. J. Ment. Health Promot. 2023, 25, 21–29. [CrossRef]
17. Zarrabeitia-Bilbao, E.; Rio-Belver, R.M.; Alvarez-Meaza, I.; de Alegría-Mancisidor, I.M. World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining. Soc. Indic. Res. 2022, 164, 263–284. [CrossRef]
18. Alhuzali, H.; Zhang, T.; Ananiadou, S. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis. J. Med Internet Res. 2022, 24, e40323. [CrossRef]
19. Behzadidoost, R.; Hasheminezhad, M.; Farshi, M.; Derhami, V.; Alamiyan-Harandi, F. A framework for text mining on Twitter: A case study on joint comprehensive plan of action (JCPOA)-between 2015 and 2019. Qual. Quant. 2022, 56, 3053–3084. [CrossRef]
20. Arumugam, S.S. Development of argument based opinion mining model with sentimental data analysis from twitter content. Concurr. Comput. Pract. Exp. 2022, 34, e6956. [CrossRef]
21. Jiang, J.Y.; Zhou, Y.; Chen, X.; Jhou, Y.R.; Zhao, L.; Liu, S.; Yang, P.C.; Ahmar, J.; Wang, W. COVID-19 Surveiller: Toward a robust and effective pandemic surveillance system based on social media mining. Philos. Trans. R. Soc. A 2022, 380, 20210125. [CrossRef]
22. Vukmirovic, M.; Raspopovic Milic, M.; Jovic, J. Twitter Data Mining to Map Pedestrian Experience of Open Spaces. Appl. Sci. 2022, 12, 4143. [CrossRef]
23. Khetarpaul, S.; Sharma, D.; Jose, J.I.; Saragur, M. Real-Time Detection and Visualization of Traffic Conditions by Mining Twitter Data. In Proceedings of the Australasian Database Conference, Sydney, Australia, 3–4 September 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 141–152.
24. de Bruijn, J.A.; de Moel, H.; Jongman, B.; de Ruiter, M.C.; Wagemaker, J.; Aerts, J.C. A global database of historic and real-time flood events based on social media. Sci. Data 2019, 6, 1–12. [CrossRef]
25. De Bruijn, J.A.; de Moel, H.; Jongman, B.; Wagemaker, J.; Aerts, J.C. TAGGS: Grouping tweets to improve global geoparsing for disaster response. J. Geovisualization Spat. Anal. 2018, 2, 1–14. [CrossRef]
26. Sarker, A.; O’connor, K.; Ginn, R.; Scotch, M.; Smith, K.; Malone, D.; Gonzalez, G. Social media mining for toxicovigilance: automatic monitoring of prescription medication abuse from Twitter. Drug Saf. 2016, 39, 231–240. [CrossRef] [PubMed]
27. Gerber, M.S. Predicting crime using Twitter and kernel density estimation. Decis. Support Syst. 2014, 61, 115–125. [CrossRef]
28. Isermann, R. Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems; Springer Science & Business Media: Berlin, Germany, 2011.
29. Gonzalez-Jimenez, D.; Del-Olmo, J.; Poza, J.; Garramiola, F.; Madina, P. Data-driven fault diagnosis for electric drives: A review. Sensors 2021, 21, 4024. [CrossRef]
30. Tinka, A.; Rafiee, M.; Bayen, A.M. Floating sensor networks for river studies. IEEE Syst. J. 2012, 7, 36–49. [CrossRef]
31. Canepa, E.; Odat, E.; Dehwah, A.; Mousa, M.; Jiang, J.; Claudel, C. A sensor network architecture for urban traffic state estimation with mixed eulerian/lagrangian sensing based on distributed computing. In Proceedings of the International Conference on Architecture of Computing Systems, Lubeck, Germany, 25–28 February 2014; Springer: Berlin/Heidelberg, Germany, 2014; pp. 147–158.
32. Hirt, C.; Amsden, A.; Cook, J. An arbitrary Lagrangian-Eulerian computing method for all flow speeds. J. Comput. Phys. 1974, 14, 227–253. [CrossRef]
33. Goodchild, M.F. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211–221. [CrossRef]
34. Yoon, S.; Elhadad, N.; Bakken, S. A practical approach for content mining of tweets. Am. J. Prev. Med. 2013, 45, 122–129. [CrossRef]
35. Ralston, M.R.; O’Neill, S.; Wigmore, S.J.; Harrison, E.M. An exploration of the use of social media by surgical colleges. Int. J. Surg. 2014, 12, 1420–1427. [CrossRef]
36. Kayed, M.; Dakrory, S.; Ali, A.A. Postal address extraction from the web: A comprehensive survey. Artif. Intell. Rev. 2021, 55, 1085–1120. [CrossRef]
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dc.rights.eng.fl_str_mv © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
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rights_invalid_str_mv Atribución 4.0 Internacional (CC BY 4.0)
© 2023 by the authors. Licensee MDPI, Basel, Switzerland.
https://creativecommons.org/licenses/by/4.0/
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jiménez-Cabas, Javier66d87f68c554f0e0264e1923d8d87067600Torres, Lizethf07be041f7f2bb857b97e78e06f6bb82600Lozoya-Santos, Jorge de Jesús247aaf6629ba601a60ab807e0d7de18a6002023-03-16T15:46:42Z2023-03-16T15:46:42Z2023-03-14Jiménez-Cabas, J.; Torres, L.; Lozoya-Santos, J.J. Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks. Sustainability 2023, 15, 5113. https://doi.org/10.3390/ su15065113https://hdl.handle.net/11323/996410.3390/ su150651132071-1050Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks.16 páginasapplication/pdfengMDPI AGSwitzerlandhttps://www.mdpi.com/2071-1050/15/6/5113Twitter data mining for the diagnosis of leaks in drinking water distribution networksArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Sustainability1. Ling, T. A Global Study about Water Crisis. In Proceedings of the 2021 International Conference on Social Development and Media Communication (SDMC 2021), Sanya, China, 26–28 November 2021; Atlantis Press: Paris , France, 2022; pp. 809–814.2. Briseño, H.; Sánchez, A. Decentralization, consolidation, and crisis of urban water management in Mexico. Tecnol. y Cienc. Del Agua 2018, 9, 25–47. [CrossRef]3. Khalifa, D.S.; El Atty, A.; Donia, N.S.; Moussa, A.; Mohamed, A. Analysis and Assessment of Water Losses in Domestic Water Distribution Networks. J. Environ. Sci. 2022, 51, 1–23. [CrossRef]4. Verde, C.; Torres, L. Modeling and Monitoring of Pipelines and Networks: Advanced Tools for Automatic Monitoring and Supervision of Pipelines; Springer: Berlin/Heidelberg, Germany, 2017; Volume 7.5. Carpentier, P.; Cohen, G. State estimation and leak detection in water distribution networks. Civ. Eng. Syst. 1991, 8, 247–257. [CrossRef]6. Pérez, R.; Puig, V.; Pascual, J.; Quevedo, J.; Landeros, E.; Peralta, A. Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Eng. Pract. 2011, 19, 1157–1167. [CrossRef]7. Soldevila, A.; Blesa, J.; Tornil-Sin, S.; Duviella, E.; Fernandez-Canti, R.M.; Puig, V. Leak localization in water distribution networks using a mixed model-based/data-driven approach. Control Eng. Pract. 2016, 55, 162–173. [CrossRef]8. Li, X.; Wen, Y.; Jiang, J.; Daim, T.; Huang, L. Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data. Technol. Forecast. Soc. Chang. 2022, 184, 122042. [CrossRef]9. Sakaki, T.; Okazaki, M.; Matsuo, Y. Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA, 26–30 April 2010; pp. 851–860.10. Jordan, S.E.; Hovet, S.E.; Fung, I.C.H.; Liang, H.; Fu, K.W.; Tse, Z.T.H. Using Twitter for public health surveillance from monitoring and prediction to public response. Data 2018, 4, 6. [CrossRef]11. Bonifazi, G.; Breve, B.; Cirillo, S.; Corradini, E.; Virgili, L. Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach. Inf. Process. Manag. 2022, 59, 103095. [CrossRef]12. Pascual-Ferrá, P.; Alperstein, N.; Barnett, D.J. Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication. Disaster Med. Public Health Prep. 2022, 16, 561–569. [CrossRef] [PubMed]13. Pilaˇrová, L.; Kvasniˇcková Stanislavská, L.; Pilaˇr, L.; Balcarová, T.; Pitrová, J. Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats. Foods 2022, 11, 2695. [CrossRef] [PubMed]14. Rahman, S.; Jahan, N.; Sadia, F.; Mahmud, I. Social crisis detection using Twitter based text mining-a machine learning approach. Bull. Electr. Eng. Inform. 2023, 12, 1069–1077. [CrossRef]15. Qorib, M.; Oladunni, T.; Denis, M.; Ososanya, E.; Cotae, P. COVID-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination twitter dataset. Expert Syst. Appl. 2023, 212, 118715. [CrossRef]16. Choi, Y.J.C.E.J. The Early Emotional Responses and Central Issues of People in the Epicenter of the COVID-19 Pandemic: An Analysis from Twitter Text Mining. Int. J. Ment. Health Promot. 2023, 25, 21–29. [CrossRef]17. Zarrabeitia-Bilbao, E.; Rio-Belver, R.M.; Alvarez-Meaza, I.; de Alegría-Mancisidor, I.M. World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining. Soc. Indic. Res. 2022, 164, 263–284. [CrossRef]18. Alhuzali, H.; Zhang, T.; Ananiadou, S. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis. J. Med Internet Res. 2022, 24, e40323. [CrossRef]19. Behzadidoost, R.; Hasheminezhad, M.; Farshi, M.; Derhami, V.; Alamiyan-Harandi, F. A framework for text mining on Twitter: A case study on joint comprehensive plan of action (JCPOA)-between 2015 and 2019. Qual. Quant. 2022, 56, 3053–3084. [CrossRef]20. Arumugam, S.S. Development of argument based opinion mining model with sentimental data analysis from twitter content. Concurr. Comput. Pract. Exp. 2022, 34, e6956. [CrossRef]21. Jiang, J.Y.; Zhou, Y.; Chen, X.; Jhou, Y.R.; Zhao, L.; Liu, S.; Yang, P.C.; Ahmar, J.; Wang, W. COVID-19 Surveiller: Toward a robust and effective pandemic surveillance system based on social media mining. Philos. Trans. R. Soc. A 2022, 380, 20210125. [CrossRef]22. Vukmirovic, M.; Raspopovic Milic, M.; Jovic, J. Twitter Data Mining to Map Pedestrian Experience of Open Spaces. Appl. Sci. 2022, 12, 4143. [CrossRef]23. Khetarpaul, S.; Sharma, D.; Jose, J.I.; Saragur, M. Real-Time Detection and Visualization of Traffic Conditions by Mining Twitter Data. In Proceedings of the Australasian Database Conference, Sydney, Australia, 3–4 September 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 141–152.24. de Bruijn, J.A.; de Moel, H.; Jongman, B.; de Ruiter, M.C.; Wagemaker, J.; Aerts, J.C. A global database of historic and real-time flood events based on social media. Sci. Data 2019, 6, 1–12. [CrossRef]25. De Bruijn, J.A.; de Moel, H.; Jongman, B.; Wagemaker, J.; Aerts, J.C. TAGGS: Grouping tweets to improve global geoparsing for disaster response. J. Geovisualization Spat. Anal. 2018, 2, 1–14. [CrossRef]26. Sarker, A.; O’connor, K.; Ginn, R.; Scotch, M.; Smith, K.; Malone, D.; Gonzalez, G. Social media mining for toxicovigilance: automatic monitoring of prescription medication abuse from Twitter. Drug Saf. 2016, 39, 231–240. [CrossRef] [PubMed]27. Gerber, M.S. Predicting crime using Twitter and kernel density estimation. Decis. Support Syst. 2014, 61, 115–125. [CrossRef]28. Isermann, R. Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems; Springer Science & Business Media: Berlin, Germany, 2011.29. Gonzalez-Jimenez, D.; Del-Olmo, J.; Poza, J.; Garramiola, F.; Madina, P. Data-driven fault diagnosis for electric drives: A review. Sensors 2021, 21, 4024. [CrossRef]30. Tinka, A.; Rafiee, M.; Bayen, A.M. Floating sensor networks for river studies. IEEE Syst. J. 2012, 7, 36–49. [CrossRef]31. Canepa, E.; Odat, E.; Dehwah, A.; Mousa, M.; Jiang, J.; Claudel, C. A sensor network architecture for urban traffic state estimation with mixed eulerian/lagrangian sensing based on distributed computing. In Proceedings of the International Conference on Architecture of Computing Systems, Lubeck, Germany, 25–28 February 2014; Springer: Berlin/Heidelberg, Germany, 2014; pp. 147–158.32. Hirt, C.; Amsden, A.; Cook, J. An arbitrary Lagrangian-Eulerian computing method for all flow speeds. J. Comput. Phys. 1974, 14, 227–253. [CrossRef]33. Goodchild, M.F. Citizens as sensors: The world of volunteered geography. GeoJournal 2007, 69, 211–221. [CrossRef]34. Yoon, S.; Elhadad, N.; Bakken, S. A practical approach for content mining of tweets. Am. J. Prev. Med. 2013, 45, 122–129. [CrossRef]35. Ralston, M.R.; O’Neill, S.; Wigmore, S.J.; Harrison, E.M. An exploration of the use of social media by surgical colleges. Int. J. Surg. 2014, 12, 1420–1427. [CrossRef]36. Kayed, M.; Dakrory, S.; Ali, A.A. Postal address extraction from the web: A comprehensive survey. Artif. Intell. Rev. 2021, 55, 1085–1120. [CrossRef]161615Leak diagnosisSocial sensorsSocial network dataTwitterText miningORIGINALTwitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks.pdfTwitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks.pdfArtículoapplication/pdf3684337https://repositorio.cuc.edu.co/bitstream/11323/9964/1/Twitter%20Data%20Mining%20for%20the%20Diagnosis%20of%20Leaks%20in%20Drinking%20Water%20Distribution%20Networks.pdfc51e8d7da901832c951d5c64f491df2bMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstream/11323/9964/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTEXTTwitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks.pdf.txtTwitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks.pdf.txtExtracted 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corporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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