Short-term fire forecast based on air state gain recurrence and zero-order brown model

Abstract

Possibilities of parameterization of the zero-order Brown model for indoor air forecasting based on the current measure of air state gain recurrence are considered. The key to the zero-order parametric Brown forecasting model is the selection of the smoothing parameter, which characterizes forecast adaptability to the current air state gain recurrence measure. It is shown that for effective short-term indoor fire forecast, the Brown model parameter must be selected from the out-of-limit set defined by 1 and 2. The out-of-limit set for the Brown model parameter is an area of effective fire forecasting based on the measure of current indoor air state gain recurrence. Errors of fire forecast based on the parameterized zero-order Brown model in the case of the classical and out-of-limit sets of the model parameters are investigated using the example of ignition of various materials in a laboratory chamber. As quantitative indicators of forecast quality, the absolute and mean forecast errors exponentially smoothed with a parameter of 0.4 are investigated. It was found that for alcohol, the smoothed absolute and mean forecast errors for the classical smoothing parameter in the no-ignition interval do not exceed 20 %. At the same time, for the out-of-limit case, the indicated forecast errors are, on average, an order of magnitude smaller. Similar ratios for forecast errors remain in paper, wood and textile ignition. However, for the transition zone corresponding to the time of material ignition, a sharp decrease in the current measure of chamber air state gain recurrence is observed. It was found that for this zone, the smoothed absolute forecast error for alcohol is about 2 % if the model parameter is selected from the classical set. If the model parameter is selected from the out-of-limit set, the forecast error is about 0.2 %. The results generally demonstrate significant advantages of using the zero-order Brown parametric model with out-of-limit model parameters for indoor fire forecasting

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

“…1, 2, are explained by the various possibilities of modifying Brown's zero-order model to carry out IF STF. A known modification reported in [38] indicates that the unclustered set for the smoothing parameter makes it possible to predict non-stationary current measures of recurrence of increments of the AEP state vector quite effectively. For example, the investigated errors related to STF for materials with different rates of ignition are about an order of magnitude smaller for the unclustered set of the exponential smoothing parameter compared to the classical set.…”

“…The subject of the research was the dynamics of STF errors, characterized by the exponentially smoothed (a parameter of 0.4) values for the current absolute and average STF errors. The materials used, the research methods and the procedure are described in detail in work [38]. We studied the accuracy of IF STF one step in advance using the proposed modification of Brown's zero-order model and exponential smoothing based on experimental data.…”

“…In [37], the classical set for the value of the smoothing parameter is proposed to be expanded to an unclustered set in order to give the exponential smoothing and the corresponding Brown's model the capability to predict TS levels with random short non-stationary trends. Paper [38] reports a study into the quality of IF STF based on the use of Brown's zero-order model for the parameter of exponential smoothing from the classical and unclustered sets. It is confirmed that in the case of an unclustered set, the quality of IF STF increases.…”

“…More promising and rational are methods based on the methods of nonlinear dynamics [36,38]. Preferred for the implementation of IF STF are forecasting methods based on Brown's zero-order model [36] or its modifications.…”

Devising a self-adjusting zero-order Brown’s model for predicting irreversible processes and phenomena

“…In [15], it is shown that to improve the quality of F of irreversible PPs, the classical set for the smoothing parameter is advisable to expand. Based on this, paper [41] reports a study into the quality of F of FP based on the use of ZOBM for the smoothing parameter from the classical and beyond-the-limit sets. It is confirmed that in the case of a beyond-the-limit set, the F quality indicators increase.…”