Abstract

It is imperative for residents to have access to safe facilities after an earthquake. This ensures the safety and well-being of individuals while minimizing the risks posed by aftershocks. This research has used multi-criteria decision-making (MCDM) and network-based analysis to select and allocate emergency shelters (ESs). Several ESs are initially chosen as potential candidates. A weighting process is then used to evaluate various criteria, including proximity to the fault, fire stations, hospitals, main roads, the area of the ESs, and the population’s vulnerability. The centers are evaluated and ranked using the CRiteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The Maximized Weighted Capacitated Coverage with a Radius (MWCCR) problem is used to address location and allocation issues for varying ESs using option rankings. The findings showed that increasing the number of centers does not always lead to a higher level of service delivery, assuming a consistent service delivery radius. The distribution of the centers is more crucial. Additionally, line density analysis is used to evaluate traffic conditions in the study area, assisting in finding areas with heavy traffic flow. When the radius of access for ESs is assumed to be small, the main roads bear less additional traffic, and with the increase of the radius, the amount of traffic on the main roads gradually increases. This is valuable information for emergency services following an earthquake.

Introduction

Earthquakes, as natural disasters, can cause extensive damage. Due to their potential to cause widespread damage to critical urban infrastructure, they are a significant challenge for large cities. Therefore, organizing crisis planning, including implementing policies that govern urban decisions, is very important to respond effectively to earthquakes [1], [2]. After an earthquake, one of the priorities is to quickly and safely evacuate citizens to protect them from potential hazards such as earthquakes, fires, and the spread of infectious diseases [3]. Emergency shelters (ESs) are safe places with necessary services for evacuating citizens during earthquakes [4]. Therefore, it is crucial to construct secure and appropriately located emergency evacuation facilities to mitigate risks following an earthquake. These facilities should have sufficient resources to support affected citizens [5]. Researchers face challenges in selecting the appropriate location for emergency evacuation due to multiple criteria. The city officials should allocate a budget to adapt the facilities and create a wide range of services in these centers. Therefore, it is crucial to determine the optimal location and quantity of ESs to minimize costs. Selecting these centers’ locations involves a location–allocation problem with two components. A positioning component determines the placement of ESs and outlines how these centers serve the population in the allocation section [6], [7]. Therefore, to determine appropriate locations for ESs, a solution must be selected to address the location allocation problem.

Various approaches have been used in studies on location allocation for ES planning. Some approaches have focused on allocating ESs to citizens in a single-objective manner, considering only one criterion, such as time or distance. Many studies commonly use single-objective techniques such as p-median, p-center, and maximum coverage. Multi-objective and hierarchical models have been used in some studies. Additionally, geographic information systems (GIS) methods, such as the Voronoi diagram, can be used [8], [9].

Conclusion

ESs are essential for promoting peace and ensuring the safety of individuals during earthquakes and potential aftershocks. These centers’ locating and coverage areas should be optimized to prioritize citizen safety and maximize accessibility. This study aimed to identify and allocate ESs in a part of Tehran’s 12th district. Potential ESs were initially selected based on specific criteria, including their proximity to fault lines, fire stations, hospitals, main roads, ES areas, and population vulnerability. The criteria were weighted, and the centers were ranked using the CRITIC and TOPSIS methods.

According to the results obtained using the CRITIC method, the criteria of distance from the hospital and fire department had the highest weight (0.19). In contrast, the area criteria had the lowest weight (0.12). The TOPSIS method has been used to rank the ESs by assigning weights to the criteria. The ranked centers were utilized to address the issue of location and allocation. In the location problem, priority was given to centers with higher ratings. The location and allocation issues have been resolved for different numbers of ESs. Based on the results, the service delivery radius is assumed to remain constant. Simply increasing the number of centers does not guarantee better service delivery. The distribution is crucial. When the coverage radius was set to 500 m, increasing the number of ESs decreased the occupancy percentage of the centers. This indicates that the centers were not fully utilized because they were more than 500 meters away from the residents. If the coverage radius is small, the distribution of centers becomes more significant. This requires considering multiple centers in different locations. In cases where the radius was set at 1000 and 1500 m, there was an increase in the percentage of service delivery and occupancy of ESs. The service coverage percentage within a 1500-meter radius was only about 60%, well below the desired value of 80%. It is possible to expand the distance to increase this case’s coverage. It is important to note that as the distance from centers increases, quick and easy access becomes more complex, which is crucial during times of crisis.