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dc.contributor.authorDelgado, Alexi
dc.contributor.authorSoto, John
dc.contributor.authorValverde, Frank
dc.date.accessioned2019-08-17T03:07:59Z
dc.date.accessioned2022-02-22T12:03:58Z
dc.date.available2019-08-17T03:07:59Z
dc.date.available2022-02-22T12:03:58Z
dc.date.issued2019-07
dc.identifier.isbn978-958-52071-4-1
dc.identifier.issn2414-6390
dc.identifier.otherhttp://laccei.org/LACCEI2019-MontegoBay/meta/FP421.html
dc.identifier.urihttp://dx.doi.org/10.18687/LACCEI2019.1.1.421
dc.identifier.urihttp://axces.info/handle/10.18687/20190101_421
dc.description.abstractOMS indicated that a person should consume 100 liters of water per day to satisfy their needs. In some districts of Lima, the consumption of this resource is much higher, while elsewhere the consumption is under of the recommended. In this study, we realized an evaluation of the consumption of drinking water using the grey clustering method, which is based on the grey systems theory. The case study was carried out in 32 districts of Lima, Peru. As a result, a map on the situation of drinking water consumption was obtained. The results revealed that the districts that consume more water are San Isidro, Miraflores, La Molina, San Borja, and Lince. In addition, the districts that consume less water were Ancon, Puente Piedra, Villa el Salvador, and Independencia. Moreover, the results of the study could help to the central government or the authorities of districts to make the best decision about the equitable distribution of water to all districts of Lima city. The Grey clustering method showed interesting results and could be applied to other problems of the country, since this method considers the uncertainty into its analysis.en_US
dc.language.isoEnglishen_US
dc.publisherLACCEI, Inc.en_US
dc.rightsLACCEI License
dc.rights.urihttps://laccei.org/blog/copyright-laccei-papers/
dc.subjectDrinking water consumptionen_US
dc.subjectThe grey clustering methoden_US
dc.subjectWater computacional modelingen_US
dc.titleEvaluation of the drinking water consumption in Lima city using the grey clustering method
dc.typeArticleen_US
dc.description.countryPeruen
dc.description.institutionPontificia Universidad Católica del Perú - PUCPen
dc.description.trackEnergy, Water and Sustainable Engineeringen
dc.journal.referatopeerReview


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