Particle Swarm Optimization Based Fire Risk Valuation Model: Shopping-Mall

In view of the shortcomings of the existing small-scale shopping mall fire prediction models, the effectiveness and scalability of the prediction results, a BP neural network prediction model is constructed to improve the prediction accuracy by considering a variety of fire risk factors. On this basis, the convergence speed of the BP neural network is accelerated with the help of the particle swarm optimization (PSO) algorithm. Then, a mixed multi-factor shopping mall fire risk grade prediction model based on a PSO based back-propagation (PSO-BP) neural network model is proposed. The constructed prediction model can simultaneously consider climate factors (daily maximum temperature, daily average temperature, 24-h precipitation, continuous drought days, sunshine hours, daily average relative humidity, and daily average wind speed), landform factors (altitude, slope, slope direction, soil water content), combustible factors (vegetation type, combustible water content, ground cover load), and human factors (density of population, distance from human activity area). Based on the actual data and field measurement data collected by the sensor network of the shopping mall (Lahore, Pakistan), the validity of the proposed model was verified by a group of experiments. The results show that the model based on the training data set and the test samples can effectively predict the fire risk level; the computational complexity of the model is significantly lower than that of the BP neural network model alone.