Study on hierarchical and coordinated optimization of site selection for large-scale flood disaster preparedness material reserves

Abstract

[Objective] To address the complex spatial distribution, significant heterogeneity of demand points, and insufficient efficiency of resource allocation in emergency material reserve location under flood disaster scenarios, an optimization approach integrating demand-based zoning and hierarchical facility allocation is developed. [Methods] Multi-dimensional feature vectors of demand points were constructed from three dimensions, including hazard, exposure, and accessibility. After normalization, a self-organizing map( SOM) neural network was employed to achieve topological mapping of high-dimensional features. The within-cluster sum of squared errors was calculated based on the neuron weight space, and the optimal number of demand clusters was determined using the elbow method. At the subregional level, a primary reserve location model was established, with the objective of minimizing the product of demand weight and transportation distance. At the global level, a higher-level reserve location model was constructed for optimization. [Results] The results showed that the optimal number of demand clusters was determined to be 34. Based on the classification result of the 34 subregions, primary reserves achieved effective coverage of intraregional demand. On this basis, eight higher-level reserves were identified through global optimization to enable coordinated resource allocation. Compared with conventional method, construction cost was reduced by approximately 16. 1%, and the weighted average response time was shortened by approximately 17. 1%. [Conclusion] The results indicate that the proposed method can effectively characterize the multidimensional heterogeneity of demand points under flood disaster scenarios and achieve zoning-driven two-level optimization of reserve allocation. Through the framework of “SOM-based zoning-local optimization-global coordination,” the spatial adaptability and system efficiency of emergency resource allocation can be significantly improved.

Keywords

large-scale flood disaster / reserve site selection / cluster analysis / multi-objective optimization / demand heterogeneity / self-organizing map / emergency material reserve / influencing factors