The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Martin E Alexander - One of the best experts on this subject based on the ideXlab platform.
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evaluating the 10 wind speed Rule of Thumb for estimating a wildfire s forward rate of spread against an extensive independent set of observations
Environmental Modelling and Software, 2020Co-Authors: Miguel G Cruz, Martin E Alexander, Paulo M Fernandes, Musa Kilinc, ângelo SilAbstract:Abstract The prediction of wildfire rate of spread and growth under high wind speeds and dry fuel moisture conditions is key to taking proactive actions to warn and in turn protect communities. We used two datasets of wildfires spreading under critical fire weather conditions to evaluate an existing Rule of Thumb that equates the forward rate of fire spread to 10% of the average open wind speed. The Rule predicted the observed rates of fire spread with an overall mean absolute error of 1.7 km h−1. The absolute error magnitude was consistent across the range in observed rates of fire spread, resulting in a reduction in percent error with an increase in spread rates. Mean absolute percent errors close to 20% were obtained for wildfires spreading faster than 2.0 km h−1. The implications of model errors in the forecasting of fire spread with respect to community warning and safety are discussed.
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the 10 wind speed Rule of Thumb for estimating a wildfire s forward rate of spread in forests and shrublands
Annals of Forest Science, 2019Co-Authors: Miguel G Cruz, Martin E AlexanderAbstract:The collective analysis of a relatively large number of wildfire observations documented in conifer forests, dry eucalypt forests and temperate shrublands revealed that the forward rate of fire spread is roughly 10% of the average 10-m open wind speed, provided both are expressed in the same units (e.g. km h −1 ). Knowledge of a wildfire’s forward spread rate is a prerequisite for defining adequate fire suppression strategies and to ensure timely public warnings. We wanted to investigate the possibility that a simple relationship exists that could be used as a first approximation for quickly estimating a wildfire’s spread rate simply from the open wind speed alone. We analysed data from a number of high-intensity wildfire observations (n = 118) documented in temperate shrublands, Australian dry eucalypt forests and North American conifer forests to examine the suitability and soundness of a relationship between wind speed and rate of fire spread. We also contrasted the performance of the best function against established fire spread rate models for the three fuel types. The resulting Rule of Thumb is that the forward rate of spread of wildfires burning in forests and shrublands in relatively dry conditions is approximately equal to 10% of the average 10-m open wind speed, where both values are expressed in the same units. The Rule of Thumb gives the most accurate results for dry fuel and high wind speed conditions with reduced bias and mean relative errors lower than 50%. Under these conditions, the error statistics are comparable to those obtained by the established fire spread rate models. The Rule is not applicable to grasslands.
Miguel G Cruz - One of the best experts on this subject based on the ideXlab platform.
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evaluating the 10 wind speed Rule of Thumb for estimating a wildfire s forward rate of spread against an extensive independent set of observations
Environmental Modelling and Software, 2020Co-Authors: Miguel G Cruz, Martin E Alexander, Paulo M Fernandes, Musa Kilinc, ângelo SilAbstract:Abstract The prediction of wildfire rate of spread and growth under high wind speeds and dry fuel moisture conditions is key to taking proactive actions to warn and in turn protect communities. We used two datasets of wildfires spreading under critical fire weather conditions to evaluate an existing Rule of Thumb that equates the forward rate of fire spread to 10% of the average open wind speed. The Rule predicted the observed rates of fire spread with an overall mean absolute error of 1.7 km h−1. The absolute error magnitude was consistent across the range in observed rates of fire spread, resulting in a reduction in percent error with an increase in spread rates. Mean absolute percent errors close to 20% were obtained for wildfires spreading faster than 2.0 km h−1. The implications of model errors in the forecasting of fire spread with respect to community warning and safety are discussed.
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the 10 wind speed Rule of Thumb for estimating a wildfire s forward rate of spread in forests and shrublands
Annals of Forest Science, 2019Co-Authors: Miguel G Cruz, Martin E AlexanderAbstract:The collective analysis of a relatively large number of wildfire observations documented in conifer forests, dry eucalypt forests and temperate shrublands revealed that the forward rate of fire spread is roughly 10% of the average 10-m open wind speed, provided both are expressed in the same units (e.g. km h −1 ). Knowledge of a wildfire’s forward spread rate is a prerequisite for defining adequate fire suppression strategies and to ensure timely public warnings. We wanted to investigate the possibility that a simple relationship exists that could be used as a first approximation for quickly estimating a wildfire’s spread rate simply from the open wind speed alone. We analysed data from a number of high-intensity wildfire observations (n = 118) documented in temperate shrublands, Australian dry eucalypt forests and North American conifer forests to examine the suitability and soundness of a relationship between wind speed and rate of fire spread. We also contrasted the performance of the best function against established fire spread rate models for the three fuel types. The resulting Rule of Thumb is that the forward rate of spread of wildfires burning in forests and shrublands in relatively dry conditions is approximately equal to 10% of the average 10-m open wind speed, where both values are expressed in the same units. The Rule of Thumb gives the most accurate results for dry fuel and high wind speed conditions with reduced bias and mean relative errors lower than 50%. Under these conditions, the error statistics are comparable to those obtained by the established fire spread rate models. The Rule is not applicable to grasslands.
Thomas Laubach - One of the best experts on this subject based on the ideXlab platform.
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Rule of Thumb behaviour and monetary policy
European Economic Review, 2003Co-Authors: Jeffery D Amato, Thomas LaubachAbstract:Abstract We investigate the implications of Rule-of-Thumb behaviour by consumers or price setters for optimal monetary policy and simple interest rate Rules. This behaviour leads to endogenous persistence in output and inflation and alters the policymaker's welfare objective. Our main finding is that highly inertial policy is optimal regardless of what fraction of agents occasionally follow a Rule of Thumb. We also find that a first-difference version of Taylor's (Carnegie–Rochester Conf. Ser. Public Policy 39 (1993) 195–214) Rule generally has desirable properties. By contrast, the coefficients in other optimised simple Rules tend to be extremely sensitive with respect to the fraction of Rule-of-Thumb behaviour.
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Rule-of-Thumb Behaviour and Monetary Policy
Social Science Research Network, 2001Co-Authors: Jeffery D Amato, Thomas LaubachAbstract:We investigate the implications of Rule-of-Thumb behaviour on the part of consumers or price setters for optimal monetary policy and simple interest rate Rules. The existence of such behaviour leads to endogenous persistence in output and inflation; changes the transmission of shocks to these variables; and alters the policymaker's welfare objective. Our main finding is that highly inertial policy is optimal regardless of what fraction of agents occasionally follow a Rule of Thumb. We also find that the interest rate Rule that implements optimal policy in the purely optimising case, and a first-difference version of Taylor's (1993) Rule, have desirable properties in all of the cases we consider. By contrast, the coefficients in other optimised simple Rules tend to be extremely sensitive with respect to the fraction of Rule-of-Thumb behaviour and changes in other parameters of the model.
ângelo Sil - One of the best experts on this subject based on the ideXlab platform.
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evaluating the 10 wind speed Rule of Thumb for estimating a wildfire s forward rate of spread against an extensive independent set of observations
Environmental Modelling and Software, 2020Co-Authors: Miguel G Cruz, Martin E Alexander, Paulo M Fernandes, Musa Kilinc, ângelo SilAbstract:Abstract The prediction of wildfire rate of spread and growth under high wind speeds and dry fuel moisture conditions is key to taking proactive actions to warn and in turn protect communities. We used two datasets of wildfires spreading under critical fire weather conditions to evaluate an existing Rule of Thumb that equates the forward rate of fire spread to 10% of the average open wind speed. The Rule predicted the observed rates of fire spread with an overall mean absolute error of 1.7 km h−1. The absolute error magnitude was consistent across the range in observed rates of fire spread, resulting in a reduction in percent error with an increase in spread rates. Mean absolute percent errors close to 20% were obtained for wildfires spreading faster than 2.0 km h−1. The implications of model errors in the forecasting of fire spread with respect to community warning and safety are discussed.
Kannan Soundararajan - One of the best experts on this subject based on the ideXlab platform.
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a Rule of Thumb for riffle shuffling
Annals of Applied Probability, 2011Co-Authors: Sami Assaf, Persi Diaconis, Kannan SoundararajanAbstract:We study how many riffle shuffles are required to mix n cards if only certain features of the deck are of interest, for example, suits disregarded or only the colors of interest. For these features the number of shuffles drops from (3/2) log2 n to log2 n. We derive closed formulae and an asymptotic “Rule of Thumb” formula which is remarkably accurate.
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A Rule of Thumb for riffle shuffling
arXiv: Probability, 2009Co-Authors: Sami Assaf, Persi Diaconis, Kannan SoundararajanAbstract:We study how many riffle shuffles are required to mix n cards if only certain features of the deck are of interest, e.g. suits disregarded or only the colors of interest. For these features, the number of shuffles drops from 3/2 log_2(n) to log_2(n). We derive closed formulae and an asymptotic `Rule of Thumb' formula which is remarkably accurate.
