Optimizing forest fire prevention resource allocation strategies based on data analysis technology
This study focuses on the main issues in the allocation of forest fire prevention resources in China,including uneven regional distribution of resources,supply-demand imbalance,and low utilization efficiency.A comprehensive forest fire prevention database was first established to collect and analyze relevant data.Subsequently,a resource demand forecasting model was developed using machine learning algorithms to enhance the scientific and accuracy of resource allocation.Based on the model's output,three main optimization strategies were proposed:delineating key protection areas,establishing dynamic allocation mechanisms,and constructing multi-party joint prevention systems.The results indicate that the implementation of these strategies can effectively improve the efficiency of forest fire prevention resource allocation,reduce fire losses,and positively impact ecology,society,and the economy.
forest fire preventiondata analysisoptimal allocation of resourcesbenefit evaluation
NETL
NSTL
万方数据
