dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2014-05-27T11:24:50Z | |
dc.date.accessioned | 2022-10-05T18:23:14Z | |
dc.date.available | 2014-05-27T11:24:50Z | |
dc.date.available | 2022-10-05T18:23:14Z | |
dc.date.created | 2014-05-27T11:24:50Z | |
dc.date.issued | 2010-11-25 | |
dc.identifier | 2010 IEEE World Congress on Computational Intelligence, WCCI 2010. | |
dc.identifier | http://hdl.handle.net/11449/71968 | |
dc.identifier | 10.1109/FUZZY.2010.5584172 | |
dc.identifier | WOS:000287453602073 | |
dc.identifier | 2-s2.0-78549261727 | |
dc.identifier | 6249842109354856 | |
dc.identifier | 0000-0002-2042-018X | |
dc.identifier | 0000-0003-3390-8747 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3921078 | |
dc.description.abstract | Due to growing urbanization and industrialization, the environment is suffering from pollution of rivers, degradation of soils and deteriorated air quality. Quality indices appear to be useful to evaluate the conditions of these media. The aim of this study was the development of a water quality index using a fuzzy inference system, since such an approach has proved advantageous in addressing problems that are subjective by nature or for which the data are uncertain. The methodology employed was based on this inference system, and considered the nine water quality parameters employed by CETESB (Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil) to evaluate water quality. After assessment of the data using the index, a comparison was made with the WQI (Water Quality Index), which is used for the monitoring of various water bodies, including in the study region. The results obtained using the index developed on the basis of fuzzy inference were found to be more useful than those derived from the method currently used by CETESB, since losses and/or omissions concerning individual parameters were minimized. © 2010 IEEE. | |
dc.language | eng | |
dc.relation | 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 | |
dc.rights | Acesso aberto | |
dc.source | Scopus | |
dc.subject | Fuzzy inference systems | |
dc.subject | Inference systems | |
dc.subject | Quality indices | |
dc.subject | Water quality indexes | |
dc.subject | Water quality parameters | |
dc.subject | Waterbodies | |
dc.subject | Air quality | |
dc.subject | Artificial intelligence | |
dc.subject | Fish | |
dc.subject | Fuzzy inference | |
dc.subject | Fuzzy systems | |
dc.subject | Quality assurance | |
dc.subject | Quality control | |
dc.subject | Water quality | |
dc.subject | River pollution | |
dc.title | Development of a water quality index using a fuzzy logic: A case study for the sorocaba river | |
dc.type | Trabalho apresentado em evento | |