Demand Prediction of Earthquake Disaster Rescue Equipment Based on SSA-BP

In order to accurately predict the demand of post-earthquake rescue equipment and improve the efficiency of earthquake emergency rescue. By combing through the historical earthquake rescue information, taking the number of victims as the forecast object, and selecting the earthquake magnitude, focal depth, earthquake intensity, earthquake onset time, disaster scope, number of collapsed houses, population density and casualties as the influencing factors, a prediction model of the number of victims was established based on the improved Sparrow optimization algorithm (ISSA) and backpropagation (BP) neural network. Based on the quantitative relationship between the number of victims and rescue equipment, the demand for earthquake rescue equipment is predicted indirectly. The results show that the ISA-BP model has higher accuracy in predicting the number of disaster victims, and has certain effectiveness in predicting the demand of rescue equipment after earthquake, and can provide reference for the dispatch of rescue equipment.