Dynamic Tuning of a Forest Fire Prediction Parallel Method

Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obte...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Caymes Scutari, Paola, Tardivo, María, Bianchini, Germán, Méndez Garabetti, Miguel
Formato: Artículo acceptedVersion
Lenguaje:Inglés
Publicado: 2023
Materias:
Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
Acceso en línea:http://hdl.handle.net/20.500.12272/8073
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-8073
record_format dspace
spelling I68-R174-20.500.12272-80732023-06-21T16:11:11Z Dynamic Tuning of a Forest Fire Prediction Parallel Method Caymes Scutari, Paola Tardivo, María Bianchini, Germán Méndez Garabetti, Miguel Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires. Universidad Tecnológica Nacional. Facultad Regional Mendoza; Argentina Peer Reviewed 2023-06-21T16:11:11Z 2023-06-21T16:11:11Z 2020-01-01 info:eu-repo/semantics/article acceptedVersion Springer Nature Switzerland AG 2020 http://hdl.handle.net/20.500.12272/8073 10.1007/978-3-030-48325-8_2 eng openAccess http://creativecommons.org/publicdomain/zero/1.0/ CC0 1.0 Universal Universidad Tecnológica Nacional. Facultad Regional Mendoza Atribución pdf Computer Science 1184, 19-34. (2020)
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
spellingShingle Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
Caymes Scutari, Paola
Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
Dynamic Tuning of a Forest Fire Prediction Parallel Method
topic_facet Dynamic tuning, Fire prediction, Differential Evolution, Parallel computing
description Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.
format Artículo
acceptedVersion
author Caymes Scutari, Paola
Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
author_facet Caymes Scutari, Paola
Tardivo, María
Bianchini, Germán
Méndez Garabetti, Miguel
author_sort Caymes Scutari, Paola
title Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_short Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_full Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_fullStr Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_full_unstemmed Dynamic Tuning of a Forest Fire Prediction Parallel Method
title_sort dynamic tuning of a forest fire prediction parallel method
publishDate 2023
url http://hdl.handle.net/20.500.12272/8073
work_keys_str_mv AT caymesscutaripaola dynamictuningofaforestfirepredictionparallelmethod
AT tardivomaria dynamictuningofaforestfirepredictionparallelmethod
AT bianchinigerman dynamictuningofaforestfirepredictionparallelmethod
AT mendezgarabettimiguel dynamictuningofaforestfirepredictionparallelmethod
_version_ 1769355078620151808

Ejemplares similares