Forest fire forecasting using fuzzy logic models
In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) an...
| Autores: | , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/354853 |
| Acesso em linha: | https://hdl.handle.net/2117/354853 https://dx.doi.org/10.3390/f12081005 |
| Access Level: | acceso abierto |
| Palavra-chave: | Fuzzy logic Fuzzy systems Forest fires Hybrid fuzzy techniques FIR ANFIS Forest fire Burned areas prediction Lògica difusa Sistemes borrosos Incendis forestals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica |
| Resumo: | In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature. |
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