Abstract
In recent years, urban buildings have become taller, occupying more and more areas, frequent fires, and increasingly difficult fire rescue tasks. Predicting fire risks in advance will help fire rescue work. Therefore, this paper proposes a fire risk prediction based on the ARIMA model. By analyzing the fire rescue data of a certain place from 2016 to 2020 and based on the data from January 1, 2016, to December 31, 2019, an ARIMA model for predicting the number of fire rescue polices was established. The data from January 1, 2020, to December 31, 2020, are used as the validation data set of the model to evaluate the accuracy and stability of the model. The results show that the ARIMA model can be better applied to fire rescue prediction and provides a scientific prediction method for the research of smart fire rescue work.
Highlights
With the rapid development of our country’s economy, the environmental complexity has risen sharply, various accidents and disasters have occurred frequently, and safety risks have continued to increase. e challenges of fire prevention, control, firefighting, and rescue problems such as physical, underground engineering, and large chemical companies will be even more severe
With the rapid expansion of the city, the number and density of urban buildings are growing at a high rate. e complex function and structure of modern urban buildings bring a great difficulty to fire control
To predict the fire risk more accurately, this paper proposes a fire risk prediction and analysis method based on the ARIMA model. e time series analysis method is used to observe the changing trend of the number of fire rescue in a certain interval, mine the rule from the data, and predict the number of fire rescue alarms in the future. e significance of our work is as follows. (1) is paper proposes a fire risk prediction and analysis method based on the ARIMA model
Summary
With the rapid development of our country’s economy, the environmental complexity has risen sharply, various accidents and disasters have occurred frequently, and safety risks have continued to increase. e challenges of fire prevention, control, firefighting, and rescue problems such as physical, underground engineering, and large chemical companies will be even more severe. By analyzing the fire and rescue data of a certain place, an ARIMA model is established to predict the number of times of fire and rescue police. Time series forecasting, neural network, and polynomial fitting are some of the most commonly used methods for predicting and analyzing fire risks Assessing such multivariate environmental disasters has always been difficult because modeling can be skewed by a variety of factors, including the quality and quantity of input parameters, the training process, and hyperparameter default settings. Both of these functions have tailing properties, which are consistent with the characteristics of the ARIMA model [19].
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