Dispatching firefighting resources effectively plays a vital role in wildfire management. To control the fire in a timely manner, resources should be dispatched in an effective and reasonable way. Moreover, the relationship between various resource-dispatching processes should be intuitive for firefighters to make decisions. In this paper, we propose a novel event-response tree-based model to dispatch different kinds of firefighting resources based on the fire suppression index (SI), which evaluates the effect of fire suppression by considering the time, cost, and effect of dispatching resources. To validate the proposed method, we compared it with the widely used mixed-integer programming (MIP) by using the historical fire data of Nanjing Laoshan National Forest Park. The results showed that the E-R tree-based resource scheduling can effectively schedule resources as well as the MIP model. Moreover, the relationship between various resource-dispatching processes in the proposed model is clear and intuitive for firefighters to make decisions.

The initial attack is a critical phase in firefighting efforts, where the first batch of resources are deployed to prevent the spread of the fire. This study aimed to analyze and understand the factors that impact the success of the initial attack, and used three machine learning models—logistic regression, XGBoost, and artificial neural network—to simulate the success rate of the initial attack in a specific region. The performance of each machine learning model was evaluated based on accuracy, AUC (Area Under the Curve), and F1 Score, with the results showing that the XGBoost model performed the best. In addition, the study also considered the impact of weather conditions on the initial attack success rate by dividing the scenario into normal weather and extreme weather conditions. This information can be useful for forest fire managers as they plan resource allocation, with the goal of improving the success rate of the initial attack in the area.