Fire Risk Assessment Using Neural Network and Logistic Regression

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

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Application of BP Neural Network in Fire Risk Assessment of Comprehensive Shopping Mall

2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS) ◽

10.1109/icicas48597.2019.00093 ◽

2019 ◽

Author(s):

Xinyu Zheng ◽

Po Hu ◽

Jianning Gao

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Bp Neural Network ◽

Fire Risk ◽

Shopping Mall ◽

Fire Risk Assessment ◽

In Fire

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Spatiotemporal Analysis of Active Fires in the Arctic Region during 2001–2019 and a Fire Risk Assessment Model

Fire ◽

10.3390/fire4030057 ◽

2021 ◽

Vol 4 (3) ◽

pp. 57

Author(s):

Zhen Zhang ◽

Leilei Wang ◽

Naiting Xue ◽

Zhiheng Du

Keyword(s):

Risk Assessment ◽

Logistic Regression ◽

Vegetation Type ◽

Fire Risk ◽

Fire Frequency ◽

Assessment Model ◽

The Arctic ◽

Polar Regions ◽

Risk Assessment Model ◽

Fire Risk Assessment

The increasing frequency of active fires worldwide has caused significant impacts on terrestrial, aquatic, and atmospheric systems. Polar regions have received little attention due to their sparse populations, but active fires in the Arctic cause carbon losses from peatlands, which affects the global climate system. Therefore, it is necessary to focus on the spatiotemporal variations in active fires in the Arctic and to assess the fire risk. We used MODIS C6 data from 2001 to 2019 and VIIRS V1 data from 2012 to 2019 to analyse the spatiotemporal characteristics of active fires and establish a fire risk assessment model based on logistic regression. The trends in active fire frequency based on MODIS C6 and VIIRS V1 data are consistent. Throughout the Arctic, the fire frequency appears to be fluctuating and overall increasing. Fire occurrence has obvious seasonality, being concentrated in summer (June–August) and highest in July, when lightning is most frequent. The frequency of active fires is related to multiple factors, such as vegetation type, NDVI, elevation, slope, air temperature, precipitation, wind speed, and distances from roads and settlements. A risk assessment model was constructed based on logistic regression and found to be accurate. The results are helpful in understanding the risk of fires in the Arctic under climate change and provide a scientific basis for fire prediction and control and for reducing fire-related carbon emissions.

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