Layout Optimization of Urban Emergency Shelter Sites Under Compound Disaster Scenarios Based on MOGWO
Abstract
To address the uncertainties in emergency shelter siting under compound disaster scenarios, based on the connotative characteristics and formation mechanisms of urban natural hazards, a hazard assessment system for geological and flood disasters was constructed using the Random Forest (RF) algorithm. On this basis, considering the triggering relationships between disasters, the hazard intensity of disaster chains was adjusted by using a multi-hazard coupling incentive model, and the comprehensive hazard index of geological-flood compound disasters was calculated. Then, from the perspectives of accessibility and safety, a comprehensive analysis of the suitability of candidate emergency shelter sites was conducted via the Gaussian Two-step Floating Catchment Area (G2SFCA) method, where the comprehensive hazard assessment coefficient of geological-flood compound disasters was incorporated as a weighting factor affecting the suitability evaluation. Furthermore, an urban emergency shelter siting model was established by using the Multi-Objective Gray Wolf Optimizer (MOGWO). Taking Sanya City as a case study, the results show that: (1) The estimation of area under the curve (AUC) of the single hazard assessment models for geological and flood disasters constructed by the RF were 0.904 and 0.899, respectively. The high-hazard zones of geological-flood compound disasters were mainly concentrated in the mountain-valley transition zones of Tianya District and Jiyang District, as well as the potential storm surge-affected zones along the southern coast. (2) The model constructed based on the MOGWO under the influence of compound disasters could effectively make up for the deficiencies in the spatial layout of current shelter siting schemes and achieve effective connection between hazard assessment and spatial planning. The methods mentioned provide a scientific basis for risk zoning control in urban territorial spatial planning and disaster prevention and mitigation in emergency management.