A novel historical data-based method for predicting firefighters demand in urban fires

Abstract

Urban fires are a prevalent type of fire that requires prompt and effective responses. Determining the appropriate number of firefighters at the alarm time is crucial, but it mostly relies on the experience of decision-makers. Due to the fuzziness and limitations of human knowledge, there is often a deviation between the number of firefighters dispatched relying on personal subjective experience and the realistic demand. This paper proposed a historical data-based method for predicting the demand number of firefighters in urban fire. Initially, the National Fire Incident Reporting System (NFIRS) data and Global Historical Climatology Network Daily (GHCN-D) weather data were combined to create a fused dataset. The dataset was subjected to anomaly detection and feature selection, then the processed data was used to predict the number of firefighters using artificial neural network (ANN). In this process, the Genetic Algorithm (GA) was applied to optimize ANN structure. Finally, comparative experiments were conducted to evaluate the performance of the proposed method, which demonstrated superior accuracy in comparison to common regression models and traditional ANN. This advancement brings the number of dispatched firefighters closer to the actual demand and contributes to ensuring an adequate allocation of firefighters for urban fires. Consequently, decision-makers can make more informed decisions regarding the number of firefighters to dispatch, leading to more effective responses to urban fires.