Short-term forecast of fire in the premises based on modification of the Brown’s zero-order model

Abstract

This paper reports the rationale for the modification of Brown’s zero-order model, which ensures increased accuracy of the short-term fire forecast based on the use of the current measure of recurrence in the increments of the state of the air environment in the premises. A special feature of the proposed model modification is that the a priori model of the dynamics of the level of the time series of the measure of the current recurrence of increments in the air environment states determined by the dangerous factors of the fire has been modified. In this case, it is proposed that the new a priori model should take into consideration additionally the value of the current increments of the level of the studied time series. That makes it possible to negligibly reduce errors of the short-term forecast of fire in the premises without significantly complicating Brown’s zero-order model while retaining all its implementing advantages. The provided accuracy of the forecast for one step in advance on the basis of a time series of measures of the current recurrence of increments of the state of the air environment, determined from the experimental data during the ignition of alcohol and timber in a laboratory chamber, has been investigated. The considered quantitative indicators of forecast accuracy are the absolute and average errors exponentially smoothed with a parameter of 0.4. It has been established that for the proposed modification the value of the average absolute error does not exceed 0.02 %. That means that an error of the short-term forecast of a fire in the premises based on the proposed modification is an order of magnitude less than that in the case of using known Brown’s model at the smoothing parameter from an unclustered set. The results from the ignition of alcohol and timber in the laboratory chamber, in general, indicate significant advantages of using the proposed modification of Brown’s zero-order model for a short-term forecast of a fire in the premises.