An Implementation of the Rothermel Fire Spread Model in the R Programming Language
Vacchiano, Giorgio Ascoli, Davide
An Implementation of the Rothermel Fire Spread Model in the R Programming Language - mayo 2015 - 12 p. ; 523-535
Transcripción del resúmen realizada por el autor: This note describes an implementation of the Rothermel fire spread model in the R programming language. The main function, ros, computes the forward rate of spread at the head of a surface fire according to Rothermel fire behavior model. Additional functions are described to illustrate the potential use and expansions of the package. The function rosunc carries out uncertainty analysis of fire behavior, that has the ability of generating information-rich, probabilistic predictions, and can be coupled to spatially-explicit fire growth models using an ensemble forecasting technique. The function bestFM estimates the fit of Standard Fuel Models to observed fire rate of spread, based on absolute bias and root mean square error. Advantages of the R implementation of Rothermel model include: open-source coding, cross-platform availability, high computational efficiency, and linking to other R packages to perform complex analyses on Rothermel fire predictions.
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An Implementation of the Rothermel Fire Spread Model in the R Programming Language - mayo 2015 - 12 p. ; 523-535
Transcripción del resúmen realizada por el autor: This note describes an implementation of the Rothermel fire spread model in the R programming language. The main function, ros, computes the forward rate of spread at the head of a surface fire according to Rothermel fire behavior model. Additional functions are described to illustrate the potential use and expansions of the package. The function rosunc carries out uncertainty analysis of fire behavior, that has the ability of generating information-rich, probabilistic predictions, and can be coupled to spatially-explicit fire growth models using an ensemble forecasting technique. The function bestFM estimates the fit of Standard Fuel Models to observed fire rate of spread, based on absolute bias and root mean square error. Advantages of the R implementation of Rothermel model include: open-source coding, cross-platform availability, high computational efficiency, and linking to other R packages to perform complex analyses on Rothermel fire predictions.
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