Forest fire prediction using fuzzy prototypical knowledge discovery
- Autores
- Olivas Varela, José Ángel; Romero, Francisco Pacual
- Año de publicación
- 2000
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- An application of Zadeh’s prototype theory in the Knowledge Acquisition process, is presented here, and as a practical example, to define a method for predicting the evolution of the forest fire occurrence-danger rate in INCEND-IA: A KBS for prediction and decision support in fighting against forest fires. This method then allows us to interpret any real cyclical situation using a previously discovered paradigm and define the current period. The FPKD (Fuzzy Prototypical Knowledge Discovery) is presented as a mechanism with the aim of generating Prototypes of Data (A new set of data sufficiently representative to be able to summarize or assimilate the behavior of any of the remaining data); but the concept of prototype is a fuzzy concept and Zadeh’s Theory provides an appropriate framework for its application. Data Mining techniques have been used (decision trees, time series, clustering...). Thus, it is possible to calculate the grade of compatibility of a real situation with the prototypes and define the current period using these affinity values, with the objective of predicting the evolution of the following days
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Inteligencia Artificial
Data mining
Uncertainty, ``fuzzy,'' and probabilistic reasoning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23461
Ver los metadatos del registro completo
id |
SEDICI_27532e4f2acf45640e3825ca0a6060de |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/23461 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Forest fire prediction using fuzzy prototypical knowledge discoveryOlivas Varela, José ÁngelRomero, Francisco PacualCiencias InformáticasInteligencia ArtificialData miningUncertainty, ``fuzzy,'' and probabilistic reasoningAn application of Zadeh’s prototype theory in the Knowledge Acquisition process, is presented here, and as a practical example, to define a method for predicting the evolution of the forest fire occurrence-danger rate in INCEND-IA: A KBS for prediction and decision support in fighting against forest fires. This method then allows us to interpret any real cyclical situation using a previously discovered paradigm and define the current period. The FPKD (Fuzzy Prototypical Knowledge Discovery) is presented as a mechanism with the aim of generating Prototypes of Data (A new set of data sufficiently representative to be able to summarize or assimilate the behavior of any of the remaining data); but the concept of prototype is a fuzzy concept and Zadeh’s Theory provides an appropriate framework for its application. Data Mining techniques have been used (decision trees, time series, clustering...). Thus, it is possible to calculate the grade of compatibility of a real situation with the prototypes and define the current period using these affinity values, with the objective of predicting the evolution of the following daysI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2000-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23461enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:26Zoai:sedici.unlp.edu.ar:10915/23461Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:26.887SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Forest fire prediction using fuzzy prototypical knowledge discovery |
title |
Forest fire prediction using fuzzy prototypical knowledge discovery |
spellingShingle |
Forest fire prediction using fuzzy prototypical knowledge discovery Olivas Varela, José Ángel Ciencias Informáticas Inteligencia Artificial Data mining Uncertainty, ``fuzzy,'' and probabilistic reasoning |
title_short |
Forest fire prediction using fuzzy prototypical knowledge discovery |
title_full |
Forest fire prediction using fuzzy prototypical knowledge discovery |
title_fullStr |
Forest fire prediction using fuzzy prototypical knowledge discovery |
title_full_unstemmed |
Forest fire prediction using fuzzy prototypical knowledge discovery |
title_sort |
Forest fire prediction using fuzzy prototypical knowledge discovery |
dc.creator.none.fl_str_mv |
Olivas Varela, José Ángel Romero, Francisco Pacual |
author |
Olivas Varela, José Ángel |
author_facet |
Olivas Varela, José Ángel Romero, Francisco Pacual |
author_role |
author |
author2 |
Romero, Francisco Pacual |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Inteligencia Artificial Data mining Uncertainty, ``fuzzy,'' and probabilistic reasoning |
topic |
Ciencias Informáticas Inteligencia Artificial Data mining Uncertainty, ``fuzzy,'' and probabilistic reasoning |
dc.description.none.fl_txt_mv |
An application of Zadeh’s prototype theory in the Knowledge Acquisition process, is presented here, and as a practical example, to define a method for predicting the evolution of the forest fire occurrence-danger rate in INCEND-IA: A KBS for prediction and decision support in fighting against forest fires. This method then allows us to interpret any real cyclical situation using a previously discovered paradigm and define the current period. The FPKD (Fuzzy Prototypical Knowledge Discovery) is presented as a mechanism with the aim of generating Prototypes of Data (A new set of data sufficiently representative to be able to summarize or assimilate the behavior of any of the remaining data); but the concept of prototype is a fuzzy concept and Zadeh’s Theory provides an appropriate framework for its application. Data Mining techniques have been used (decision trees, time series, clustering...). Thus, it is possible to calculate the grade of compatibility of a real situation with the prototypes and define the current period using these affinity values, with the objective of predicting the evolution of the following days I Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
An application of Zadeh’s prototype theory in the Knowledge Acquisition process, is presented here, and as a practical example, to define a method for predicting the evolution of the forest fire occurrence-danger rate in INCEND-IA: A KBS for prediction and decision support in fighting against forest fires. This method then allows us to interpret any real cyclical situation using a previously discovered paradigm and define the current period. The FPKD (Fuzzy Prototypical Knowledge Discovery) is presented as a mechanism with the aim of generating Prototypes of Data (A new set of data sufficiently representative to be able to summarize or assimilate the behavior of any of the remaining data); but the concept of prototype is a fuzzy concept and Zadeh’s Theory provides an appropriate framework for its application. Data Mining techniques have been used (decision trees, time series, clustering...). Thus, it is possible to calculate the grade of compatibility of a real situation with the prototypes and define the current period using these affinity values, with the objective of predicting the evolution of the following days |
publishDate |
2000 |
dc.date.none.fl_str_mv |
2000-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/23461 |
url |
http://sedici.unlp.edu.ar/handle/10915/23461 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844615813783355392 |
score |
13.070432 |