[7]. Much research has addressed this problem through building location models. The primary location models include \(p\)-center model, \(p\)-median model, location set covering model, and maximal covering location model. [PERSON] first proposed the \(p\)-median model and the \(p\)-center model to minimize travel costs [8]. The \(p\)-median model is mainly used to reduce the total cost of facility configuration and the \(p\)-center model is mainly used for minimizing the maximum distance from any demand point to the facility. [PERSON] and [PERSON] first proposed the location set covering model to analyze the location problem of emergency service facilities [9; 10], i.e., fire stations and ambulances, to ensure that all the demanding points were covered and at the same time with the smallest total number of facilities or the lowest total construction cost of the facility. [PERSON] and [PERSON] put forward the maximum coverage model to determine the number of facility points with their number of facilities and coverage radius to ensure the optimal number of coverage points and maximum coverage area of each [11]. Based on these four primary models, various location models were developed, which were extended by assigning different constraint conditions and parameters. [PERSON] and [PERSON] divided the dynamic time into a number of specific periods, based on which, a dynamic model with profit maximization as the goal was established to solve the multi-stage dynamic location problem of a single warehouse [12]. [PERSON] et al. introduced a multi-objective location model to determine the number and location of emergency shelters and the evacuation routes that evacuees should follow [13]. [PERSON] et al. addressed a variation of the \(p\)-center model with the additional assumption that a facility fails to respond to the demands from the same node, which is likely to happen to many local facilities in large-scale disasters [14]. Considering the uncertainty of demand, [PERSON] et al. formulated a special case of the maximal covering location model with a loss function, to account for the distance-sensitive demand, and chance constraints to address the demand uncertainty [15]. In addition, they illustrated the use of the model in LA County. [PERSON] et al. proposed a general fuzzy multi-objective model to solve the maximal coverage problem with distance constraints, and the solution is put forward by using a GIS-supported Ant algorithm [16]. [PERSON] et al. formulated an integrated location-allocation model based on time-varying demand analysis for emergency management and applied the model in the emergency shelters planning of Shanghai [3]. [PERSON] et al. proposed a hybrid bi-level model that balances both behavioral assumptions to minimize the total evacuation time and the individual evacuation time [17]. [PERSON] and [PERSON] proposed the location model, which uses space syntax and road-closure indices to identify where to locate emergency shelters based on behavior analysis [18]. With more parameters considered, all models noted above have become more complicated, making them difficult to follow and apply. Although several scholars have put forward a variety of efficient algorithms, most of them are still limited in terms of theoretical development or the realization of a single case. The \(p\)-median model, \(p\)-center model, and coverage model are common location models with widespread applications [9; 19]. These three models laid the foundation for the construction of the location model in our manuscript. In the selection process of urban emergency facilities, an appropriate model should be chosen according to the characteristics of the facilities. A single model cannot simultaneously meet all the problems encountered in the process of emergency facility location. Thus, given the multi-level nature of different types of emergency shelters, different models are taken into account through building multi-level location models of emergency shelters.